Patent Publication Number: US-8995012-B2

Title: System for mobile image capture and processing of financial documents

Description:
FIELD OF THE INVENTION 
     The present disclosure relates generally to an automated document processing system and more particularly, a system for mobile image capture and processing of financial documents to enhance an image for data extraction from images captured on a mobile device with camera capabilities. 
     BACKGROUND OF THE INVENTION 
     In general, financial institutions have automated most check processing systems by printing financial information, such as account numbers and bank routing numbers, onto the checks. Before a check amount is deducted from a payer&#39;s account, the amount, account number, and other important information must be extracted from the check. This highly automated form of extraction is done by a check processing control system that captures information from the Magnetic Ink Character Recognition (“MICR”) line. The MICR line consists of specially designed numerals that are printed on the bottom of a check using magnetic ink. The MICR data fields include the bank routing number, bank transit number, account number, check serial number, check amount, process code and extended process code. 
     Checks and other documents may be processed by banks and other financial institutions in large numbers. The documents that may be processed might include checks, deposit slips, payment slips, etc. In some cases the banks or other financial institutions may be required to use the actual physical documents. For example, checks might need to be transported between multiple banks or other financial institutions. This may slow down the processing of financial documents. In addition, other types of documents that are non-financial in nature may be processed by businesses and other institutions in large volumes. 
     In order to facilitate processing of a document depicted in an image captured by a mobile device, image optimization and enhancement processing operations must be applied such that data can be extracted from the document. One approach to processing images captured from a mobile device is described in U.S. Pat. No. 7,778,457 to Nepomniachtchi et al., hereby incorporated by reference. 
     Nepomniachtchi et al. discloses either performing multiple image processing steps on a mobile device or transmitting large color images to a server. Mobile devices are often limited in terms of available processing power and transmission bandwidth. Performing multiple image processing operations on a mobile device can may take a long time due to the limited processing power and prevent the user from effectively performing other tasks on the mobile device. Similarly, sending images with a large file size will also take a long time and limit the communication functions of the mobile device while the image is being transmitted. 
     Nepomniachtchi et al. also discloses an algorithm for binarizing an image that applies the same algorithm to the entire document. Unfortunately, many images present complex backgrounds or weak image foregrounds (some foreground pixels have gray values very close to those of some background pixels). In such cases, it is not possible to find a single threshold or window that completely separates the foreground image from the background. This results in background noise in the bi-tonal image. In addition, certain document fields may be read by a computer process such that these areas should have limited background noise. 
     Nepomniachtchi et al. also discloses a system and method for correcting the upside-down orientation of a check within an image that relies on comparing MICR confidence from the original image with MICR confidence from a 180 degree rotated image. Relying on comparing MICR confidence readings limits the speed of the algorithm when implementing the method on a server with multi-threading/multi-processors. The approach in Nepomniachtchi et al. does not address cases where MICR confidence of both images is too low to be acceptable for subsequent processing. 
     Nepomniachtchi et al. also discloses a system and method for correcting the size of an image using the width of MICR characters. Using width of MICR characters may produce inaccurate size transformations since geometric correction can distort the shape of the MICR characters. Nepomniachtchi et al. also relies on the aspect ratio of the geometrically corrected image which may also be slightly distorted. It can also be difficult to discern the width of certain MICR characters compared to others. Nepomniachtchi et al. also does not scale the document to correspond to known or expected document or check dimensions. 
     SUMMARY OF THE INVENTION 
     Accordingly, improved systems for processing images captured by a mobile device are provided. The following descriptions are various embodiments (or aspects) that are encompassed by the invention discussed herein. 
     According to a first aspect, a system is provided for image capture and processing of financial documents from a mobile device. The mobile device includes an image capture device that is configure to capture a color image of a financial document. The mobile device also includes a processor that is configured to generate a color reduced image and a transmitter that transmits the color reduced image to a server. In some aspects, the color reduced image is a gray-scale image. The server receives the color reduced image from the mobile device and detects the financial document in the color reduced image, geometrically corrects the color reduced image, binarizes the color reduced image to produce a bi-tonal image, and corrects the orientation and size of the bi-tonal image. In some aspects, the server also corrects the orientation and size of the color reduced image. 
     According to another aspect, the server is further configured to binarize the geometrically corrected image to produce a bi-tonal image. The server chooses a pixel on the gray-scale image, determines whether the chosen pixel is located within a document field, and if the chosen pixel is within a document field, then a window is selected within the document field and an average value and standard deviation are computed for the chosen pixel over the selected window. If the standard deviation is too small the pixel is converted to white, and if the standard deviation is not too small, then converting the chosen pixel to black or white based on the intensity. The process is repeated until there are no more pixels to choose. In another aspect, a threshold is selected for the document field and the determining operation uses the threshold to determine if the standard deviation is too small. In an aspect where the document is a check, the document field may be any one of the: MICR line, courtesy amount, legal amount, date, signature and payee. 
     According to another aspect, correcting the orientation of the captured image comprises correcting the orientation of the document within the image if the document is in upside-down orientation. In some aspects, correcting the orientation of the captured image further comprises determining the orientation of the document within the image using a relevant object of a known position on the document. In some aspects, the server is further configured to correct the orientation of the bi-tonal image by reading a MICR line on the bottom of the financial document, generating a MICR confidence value for the MICR line as read, comparing the MICR confidence value to a threshold, when the MICR confidence value exceeds the threshold, determining that the bi-tonal image is right-side up, and when the MICR confidence value does not exceed the threshold: determining that the bi-tonal image is not right side up, rotating the image 180 degrees, re-reading the MICR line, generating a new MICR confidence value, comparing the new MICR confidence value to the threshold, when the new MICR confidence value exceeds the threshold, determining that the rotated, bi-tonal image is right side up. In another aspect if neither MICR confidence value exceeds the threshold then the server is further configured to indicate that the orientation of the image is unknown. 
     According to another aspect, where the financial document in the image is a check, the server is further configured to correct the size of the bi-tonal image using the MICR line. In one aspect the average height of the MICR characters is used to determine a scaling factor that is used to compute the dimensions of the image. In another aspect, the scaling factor is determined using the distance relative to MICR symbols, such as the distance between transit symbols in one aspect, or the distance between a transit symbol and the leading edge of the check in a second aspect. In another aspect, both the height and width of the MICR character are used to determine height and width scaling factors that are used to compute the dimensions of the image. In another aspect, the computed dimensions are compared to expected dimensions in order to adjust the scaling of the image. The expected dimensions may use known check dimensions or dimensions that are based on document dimensions having ⅛th inch multiples. 
     Other features and advantages should become apparent from the following description, taken in conjunction with the accompanying drawings which illustrate various embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of the various embodiments described herein and to show more clearly how they may be carried into effect, reference will now be made, by way of example only, to the accompanying drawings which show at least one exemplary embodiment, and in which: 
         FIG. 1  is a diagram illustrating an example check that might be imaged with the systems and methods described herein; 
         FIG. 2  is a diagram illustrating an example payment coupon that might be imaged using the systems and methods described herein; 
         FIG. 3  is a diagram illustrating an example out-of-focus image of the check illustrated in  FIG. 1 ; 
         FIG. 4  is a diagram illustrating an example out-of-focus image of the payment coupon illustrated in  FIG. 2 ; 
         FIG. 5  is a diagram illustrating an example of perspective distortion in an image of a rectangular shaped document; 
         FIG. 6  is a diagram illustrating an example original image, focus rectangle and document quadrangle ABCD in accordance with the example of  FIG. 5 ; 
         FIG. 7  is a flowchart illustrating an example method in accordance with the systems and methods described herein; 
         FIG. 8  is a diagram illustrating an example bi-tonal image of the check of  FIGS. 1 and 3  in accordance with the systems and methods described herein; 
         FIG. 9  is a diagram illustrating an example bi-tonal image of the payment coupon of  FIGS. 2 and 4  in accordance with the systems and methods described herein; 
         FIG. 10  is a flowchart of an example method that is used during image processing stages in accordance with the systems and methods described herein; 
         FIG. 11   a  is a flowchart illustrating a known method for automatic document detection within a color image from a mobile device in accordance with the systems and methods described herein; 
         FIG. 11   b  is an example mobile image depicting a check where the corners have been detected in accordance with the systems and methods described herein; 
         FIG. 11   c  is a flowchart illustrating an improved method for automatic document detection within a gray-scale image from a mobile device in accordance with the systems and methods described herein; 
         FIG. 12   a  is a flowchart illustrating an example method for converting a color image to a smaller “icon” image in accordance with the systems and methods described herein; 
         FIG. 12   b  is a mobile image depicting an example of the mobile image of  FIG. 11   b  after being converted into a color “icon” image in accordance with the systems and methods described herein; 
         FIG. 13   a  is a flowchart illustrating an example method for color depth reduction in accordance with the systems and methods described herein; 
         FIG. 13   b  is a mobile image depicting an example of the color “icon” image of  FIG. 12   b  after a color depth reduction operation has divided it into a 3×3 grid in accordance with the systems and methods described herein; 
         FIG. 13   c  is a mobile image depicting an example of the of the color “icon” image of  FIG. 12   b  once it has been converted to a gray “icon” image by a color depth reduction operation in accordance with the systems and methods described herein; 
         FIG. 14  is a flowchart illustrating an example method for finding document corners from a gray “icon” image in accordance with the systems and methods described herein; 
         FIG. 15   a  is a flowchart illustrating an example method for geometric correction in accordance with the systems and methods described herein; 
         FIG. 15   b  is an example mobile image depicting a check in landscape orientation; 
         FIG. 15   c  is a mobile image depicting an example of the mobile image of  FIG. 11   b  after a geometric correction operation in accordance with the systems and methods described herein; 
         FIG. 16   a  is a flowchart illustrating an example method for binarization in accordance with the systems and methods described herein; 
         FIG. 16   b  is a mobile image depicting an example of the mobile image of  FIG. 15   c  after it has been converted to a bi-tonal image by a binarization operation in accordance with the systems and methods described herein; 
         FIG. 16   c  is a flowchart illustrating additional operations to the method for binarization of  FIG. 16   a;    
         FIG. 17   a  is a flowchart illustrating a known method for correcting the upside-down orientation of a document within a mobile image in accordance with the systems and methods described herein; 
         FIG. 17   b  is an example bi-tonal image depicting a check in an upside-down orientation; 
         FIG. 17   c  is a flowchart illustrating an improved method for correcting the upside-down orientation of a document within a mobile image or indicating if orientation is unknown; 
         FIG. 18   a  is a flowchart illustrating an example method for size correction of an image using height of MICR characters in accordance with the systems and methods described herein; 
         FIG. 18   b  is a flowchart illustrating an example method for size correction an image using height and width of MICR characters in accordance with the systems and methods described herein; and 
         FIG. 19  is a simplified block diagram illustrating an example-computing module. 
     
    
    
     DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS 
     It will be appreciated that for simplicity and clarity of illustration, where considered appropriate, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the embodiments described herein. Furthermore, this description is not to be considered as limiting the scope of the embodiments described herein in any way, but rather as merely describing the implementations of various embodiments described herein. 
     The embodiments of the systems, devices and methods described herein may be implemented in hardware or software, or a combination of both. Some of the embodiments described herein may be implemented in computer programs executing on programmable computers, each computer comprising at least one processor, a computer memory (including volatile and non-volatile memory), at least one input device, and at least one output device. For example, and without limitation, the programmable computers may be a server class computer having multiple processors and at least one network interface card. Program code may operate on input data to perform the functions described herein and generate output data. 
     The present invention is directed towards automated document processing and systems and methods for document image processing using mobile devices. Generally, some embodiments of the invention capture an original color image of a document using a mobile device and then convert the color image to a bi-tonal image. More specifically, some embodiments accept an image of a document captured by a mobile device and convert it into a bi-tonal image of the document that is substantially equivalent in its resolution, size, and quality to document images produced by “standard” scanners. 
     Before describing the invention in greater detail, it would be useful to define some of the common terms used herein when describing various embodiments of the invention. 
     The term “standard scanners” includes, but is not limited to, transport scanners, flat-bed scanners, and specialized check-scanners. Some manufacturers of transport scanners include UNISYS®, BancTec®, IBM®, and Canon®. With respect to specialized check-scanners, some models include the TellerScan® TS200 and the Panini® My Vision X. Generally, standard scanners have the ability to scan and produce high quality images, support resolutions from 200 dots per inch to 300 dots per inch (DPI), produce gray-scale and bi-tonal images, and crop an image of a check from a larger full-page size image. Standard scanners for other types of documents may have similar capabilities with even higher resolutions and higher color-depth. 
     The term “color images” includes, but is not limited to, images having a color depth of 24 bits per a pixel (24 bit/pixel), thereby providing each pixel with one of 16 million possible colors. Each color image is represented by pixels and the dimensions W (width in pixels) and H (height in pixels). An intensity function I maps each pixel in the [W×H] area to its RGB-value. The RGB-value is a triple (R,G,B) that determines the color the pixel represents. Within the triple, each of the R (Red), G (Green) and B (Blue) values are integers between 0 and 255 that determine each respective color&#39;s intensity for the pixel. 
     The term “gray-scale images” includes, but is not limited to, images having a 8 bits per a pixel (8 bit/pixel) that provides one of 256 shades of gray. As a person of ordinary skill in the art would appreciate, gray-scale images also include images with color depths of other various bit levels (e.g. 4 bit/pixel or 2 bit/pixel) representing fewer shades of gray. Each gray-scale image is represented by pixels and the dimensions W (width in pixels) and H (height in pixels). An intensity function I maps each pixel in the [W×H] area onto a range of gray shades. More specifically, each pixel has a value between 0 and 255 which determines that pixel&#39;s shade of gray. 
     Bi-tonal images are similar to gray-scale images in that they are represented by pixels and the dimensions W (width in pixels) and H (height in pixels). However, each pixel within a bi-tonal image has one of two colors: black or white. Accordingly, a bi-tonal image has a color depth of 1 bit per a pixel (1 bit/pixel) representing either black or white. The similarity transformation is based off the assumption that there are two images of [W×H] and [W′×H′] dimensions, respectively, and that the dimensions are proportional (i.e. W/W′=H/H′). The term “similarity transformation” may refer to a transformation ST from [W×H] area onto [W′×H′] area such that ST maps pixel p=p(x,y) on pixel p′=p′(x′,y′) with x′=x*W/W and y=y*H′/H. 
       FIG. 1  is a diagram illustrating an example check  100  that might be imaged with the systems and methods described herein. The mobile image capture and processing systems and methods may be used with a variety of documents, including financial documents such as personal checks, business checks, cashier&#39;s checks, certified checks, and warrants. By using an image of the check  100 , the check clearing process is performed more efficiently. As would be appreciated by those of skill in the art, checks are not the only type of documents that may be used with these systems. For example, other documents, such as deposit slips, might also be processed using the systems and methods described herein.  FIG. 2  is a diagram illustrating an example payment coupon  200  that might be imaged using the systems and methods described herein. 
     In some embodiments, checks  100 , payment coupons  200 , or other documents might be imaged using a mobile device. The mobile device may be a mobile telephone handset, Personal Digital Assistant, or other mobile communication device. The mobile device may include a camera, or might include functionality that allows it to connect to a camera. This connection might be wired or wireless. In this way the mobile device may connect to an external camera and receive images from the camera. 
     Images of the documents taken using the mobile device or downloaded to the mobile device may be transmitted to a server. For example, in some cases, the images may be transmitted over a mobile communication device network, such as a code division multiple access (“CDMA”) telephone network, or other mobile telephone network. Images taken using, for example, a mobile device&#39;s camera, may be initially formatted as 24 bit per pixel (24 bit/pixel) JPEG images. It will be understood, however, that many other types of images might also be taken using different cameras, mobile devices, etc. 
     Various documents may include various fields. Some of the fields in the documents might be considered “primary” fields. For example, the primary fields of interest on a check  100  might include the courtesy amount  102 , legal amount  104  and the MICR line  106 . The MICR line  106  may include symbols that delimit fields within the MICR line, such as, for example, transit symbol  113  indicating the transit and the on-us symbol  115  indicating the account number. Other fields of interest may include the payee  108 , date  110  and the signature  112 . The primary fields of interest for the payment coupon  200  might include the payment amounts  202 , such as the balance, minimum payment and interest. The billing company name and address  204 , the account number  206  and the code-line  208  may also be fields of interest. In some embodiments it may be necessary to electronically read various information from these fields on a document. For example, in order to process a check that is to be deposited, it might be necessary to electronically read the legal  104  and courtesy  102  amounts, the MICR line  106 , the payee  108 , date  110  and the signature  112  on the check. In some cases, this information is difficult to read because, for example, the check or other document is out of focus or is otherwise poorly imaged. 
       FIG. 3  is a diagram illustrating an example out-of-focus image of the check illustrated in  FIG. 1 . In some cases, document images might be out of focus. An image of a document that is out of focus may be difficult or impossible to read, electronically process, etc. For example, it might be difficult to read the amounts  302  and  304  or the payee  306  on the image  300  of the check  100 .  FIG. 4  is a diagram illustrating an example out-of-focus image of the payment coupon illustrated in  FIG. 2 . Because the image  400  of the payment coupon  200  is out of focus it might be difficult to properly credit the payment. For example, the payment might be credited to the wrong account or an incorrect amount might be credited. This may be especially true if a check and a payment coupon are both difficult to read or the scan quality is poor. 
     Many different factors may affect the quality of an image and the ability of a mobile device based image capture and processing system. Optical defects, such as out-of-focus images (as discussed above), unequal contrast or brightness, or other optical defects, might make it difficult to process an image of a document (e.g., a check, payment coupon, deposit slip, etc.). The quality of an image may also be affected by the document position on a surface when photographed or the angle at which the document was photographed. This affects the image quality by causing the document to appear, for example, right side up, upside down, skewed, etc. Further, if a document is imaged while upside-down it might be impossible or nearly impossible to for the system to determine the information contained on the document. 
     In some cases, the type of surface might affect the final image. For example, if a document is sitting on a rough surface when an image is taken, that rough surface might show through. In some cases the surface of the document might be rough because of the surface below it. Additionally, the rough surface may cause shadows or other problems that might be picked up by the camera. These problems might make it difficult or impossible to read the information contained on the document. 
     Lighting may also affect the quality of an image, for example, the location of a light source and light source distortions. Using a light source above a document might light the document in a way that improves the image quality, while a light source to the side of the document might produce an image that is more difficult to process. Lighting from the side might, for example, cause shadows or other lighting distortions. The type of light might also be a factor, for example, sun, electric bulb, florescent lighting, etc. If the lighting is too bright, the document might be washed out in the image. On the other hand, if the lighting is too dark, it might be difficult to read the image. 
     The quality of the image might also be affected by document features, such as, the type of document, the fonts used, the colors selected, etc. For example, an image of a white document with black lettering may be easier to process than a dark colored document with black letters. Image quality may also be affected by the mobile device used. Some mobile camera phones, for example, might have cameras that save an image using a greater number of mega pixels. Other mobile cameras phones might have an auto-focus feature, automatic flash, etc. Generally, these features may improve an image when compared to mobile devices that do not include such features. 
     A document image taken using a mobile device might have one or more of the defects discussed above. These defects or others may cause low accuracy when processing the image, for example, when processing one or more of the fields on a document. Accordingly, in some embodiments, systems and methods using a mobile device to create images of documents may include the ability to identify poor quality images. If the quality of an image is determined to be poor, a user may be prompted to take another image. 
     A variety of metrics might be used to detect an out-of-focus image. For example, a focus measure may be employed. The focus measure may be the ratio of the maximum video gradient between adjacent pixels measured over the entire image and normalized with respect to an image&#39;s gray level dynamic range and “pixel pitch”. The pixel pitch may be the distance between dots on the image. In some embodiments a focus score might be used to determine if an image is adequately focused. If an image is not adequately focused, a user might be prompted to take another image. 
     An image focus score might be calculated as a function of maximum video gradient, gray level dynamic range and pixel pitch. For example, in one embodiment:
 
Image Focus Score=(Maximum Video Gradient)*(Gray Level Dynamic Range)*(Pixel Pitch)  (eq. 1)
 
     The video gradient may be the absolute value of the gray level for a first pixel “i” minus the gray level for a second pixel “i+1”. For example:
 
Video Gradient=ABS[(Grey level for pixel “ i ”)−(Gray level for pixel “ i+ 1”)]  (eq. 2)
 
     The gray level dynamic range may be the average of the “n” lightest pixels minus the average of the “n” darkest pixels. For example:
 
Gray Level Dynamic Range=[AVE(“ N ” lightest pixels)−AVE(“ N ” darkest pixels)]  (eq. 3)
 
     In equation 3 above, N may be defined as the number of pixels used to determine the average darkest and lightest pixel gray levels in the image. In some embodiments, N might be chosen to be 64. Accordingly, in some embodiments, the 64 darkest pixels are averaged together and the 64 lightest pixels are averaged together to compute the gray level dynamic range value. 
     The pixel pitch may be the reciprocal of the image resolution, for example, in dots per inch.
 
Pixel Pitch=[1/Image Resolution]  (eq. 4)
 
     In other words, as defined above, the pixel pitch is the distance between dots on the image because the Image Resolution is the reciprocal of the distance between dots on an image. 
       FIG. 5  is a diagram illustrating an example of perspective distortion in an image of a rectangular shaped document. An image may contain perspective transformation distortions  500  such that a rectangle might become a quadrangle ABCD  502 , as illustrated in the figure. The perspective distortion may occur because an image is taken using a camera that is placed at an angle to a document rather than directly above the document. When directly above a rectangular document it will generally appear to be rectangular. As the imaging device moves from directly above the surface, the document distorts until it can no longer be seen and only the edge of the page may be seen. 
     The dotted frame  504  comprises the image frame obtained by the camera. The image frame is sized h×w, as illustrated in the figure. Generally, it may be preferable to contain an entire document within the h×w frame of a single image. It will be understood, however, that some documents might be too large or include too many pages for this to be preferable or even feasible. 
     In some embodiments, an image might be processed, or preprocessed, to automatically find and “lift” the quadrangle  502 . In other words, the document that forms quadrangle  502  might be separated from the rest of the image so that the document alone might be processed. By separating quadrangle  502  from any background in an image, it may then be further processed. 
     The quadrangle  502  might be mapped onto a rectangular bitmap in order to remove or decrease the perspective distortion. Additionally, image sharpening might be used to improve the out-of-focus score of the image. The resolution of the image may then be increased and the image converted to a black-and-white image. In some cases, a black-and-white image might have a higher recognition rate when processed using an automated document processing system in accordance with the systems and methods described herein. 
     An image that is bi-tonal, e.g., black-and-white, might be used in some systems. Such systems might require an image that is at least 200 dots per inch resolution. Accordingly, a color image taken using a mobile device might need to be high enough quality so that the image may successfully be converted from, for example, a 24 bit per pixel (24 bit/pixel) RGB image to a bi-tonal image. The image may be sized as if the document, e.g., check, payment coupon, etc., was scanned at 200 dots per inch. 
       FIG. 6  is a diagram illustrating an example original image, focus rectangle and document quadrangle ABCD in accordance with the example of  FIG. 5 . In some embodiments it may be necessary to place a document for processing at or near the center of an input image close to the camera. All points A, B, C and D are located in the image, and the focus rectangle  602  is located inside quadrangle ABCD  502 . The document might also have a low out-of-focus score and the background surrounding the document might be selected to be darker than the document. In this way, the lighter document will stand out from the darker background. 
       FIG. 7  is a flowchart illustrating an example method  700  in accordance with the systems and methods described herein. Referring now to  FIG. 7 , in operation  701  a user logs into a document capture system on a mobile communication device. In accordance with various embodiments, methods and systems for document capture on a mobile communication device may further comprise requiring the user to log into an application. In this way, access to the document capture system using a mobile communication device might be limited to authorized users. 
     In operation  702 , in the illustrated embodiment, the type of document is selected. For example, a user might select a document type for a check, payment coupon or deposit slip. By entering the type of document, a mobile device might be able to scan specific parts of an image to determine, for example, payee, check amount, signature, etc. In some embodiments, however, a device might determine what type of document is being imaged by processing the image. 
     In operation  704 , an image is captured using, for example, a mobile communication device. In the illustrated embodiment an application running on the mobile communication device may prompt the user of the device to take a picture of the front of the document. The back of the document might also be imaged. For example, if the document is a check, an image of the back of the document might be necessary because the back of the check might need to be endorsed. If the back of the document needs to be imaged, the application may prompt the user to take the image. The application might also conduct some image processing to determine if the quality of the image or images is sufficient for further processing in accordance with the systems and methods described herein. The quality needed for further processing might vary from implementation to implementation. For example, some systems might be better able to determine information contained on a poor quality image than other systems. 
     In the illustrated embodiment, at operation  706 , an amount is entered. When the document being processed is a check, the amount entered may be the amount of the check. Alternatively, the amount might be an amount of a payment or an amount of a deposit, depending on the type of document being processed. 
     In some embodiments, the system might determine the amount by processing the image. For example, in some cases, optical character recognition (“OCR”) might be used to determine what characters and numbers are present on the document. For example, numbers located in the amount box of a check or payment coupon might then be determined using OCR or other computer based character determination. This might be done instead of requiring the amount to be entered manually. In other embodiments, a manual entry might be used to verify a computer generated value that is determined using, for example, OCR or other computer based character determination. 
     In operation  708 , the image is transmitted to a server. The image might be transmitted from the mobile communication device that captured the image of the document (e.g. camera phone) using, for example, hypertext transfer protocol (“HTTP”) or mobile messaging service (“MMS”). The server might then confirm that the image was received by, for example, transmitting a message back to the mobile device. 
     In operation  710 , image processing is performed. In the example embodiment, the server may clean up the image by performing auto-rotate, de-skew, perspective distortion correction, cropping, etc. The server might also process the image to produce a bi-tonal image for data extraction. 
     In other embodiments, some or all data processing might be performed at the mobile communication device. For example, the mobile communication device might perform auto-rotate, de-skew, perspective distortion correction, cropping, etc. Additionally, the mobile device might also process the image to produce a bi-tonal image for data extraction. In some cases, the processing might be shared between the mobile device and the server. 
     In operation  712 , the processing of the document using a mobile device is completed. For example, when the server has confirmed that all necessary data can be extracted from a received image, it might transmit a status message to the mobile device that transmitted the image. Alternatively, if some necessary data cannot be extracted, the server may transmit a request for additional data. This request might include a request for an additional image. In some cases, the request may be for data entered by a user, for example, an amount, e.g., of a check, that might be entered using a key pad on the mobile communication device. 
     In some embodiments, the quality of the image is determined at the mobile device. In this way the number of requests from the server for additional images might be reduced. The request may come directly from the mobile device. This may allow for the request to be more quickly determined and may allow a user to take an additional image within a shorter time from the earlier image. This may mean, for example, that the user is still physically close to the document and is still holding the communication device. This might make it easier to retake an image. If the image quality processing occurs at a server it might take longer to determine that the image quality is acceptable and communicate that information back to a user. This may mean the user is no longer near the document or has started performing another task. It will be understood, however, that in some embodiments, a server based implementation might be employed to off-load processing demands from the mobile device. Additionally, in some cases it might be quick as or quicker than a system that uses the mobile communication device to process an image to determine image quality. 
       FIG. 8  is a diagram illustrating an example bi-tonal image  800  of the check of  FIGS. 1 and 3  in accordance with the systems and methods described herein.  FIG. 9  is a diagram illustrating an example bi-tonal image  900  of the payment coupon of  FIGS. 2 and 4  in accordance with the systems and methods described herein. As illustrated, in the bi-tonal images of  FIGS. 8 and 9 , the necessary information (such as payees, amounts, account number, etc.) has been preserved, while extra information has been removed. For example, background patterns that some people might have on there checks are not present in the bi-tonal image  800  of the check. 
       FIG. 10  is a flowchart of an example method  1000  that is used during image processing stages. In particular, some or all of the operations illustrated in  FIG. 10  can be performed during various operations illustrated in  FIG. 7 . Referring now to  FIG. 10 , at operation  1001  the method  700  receives a color image originally taken by the mobile device (also referred to as the “mobile image”). For example, the image might originate from a camera phone which has now transmitted the image to a server for post-capture processing. This mobile image has a document located somewhere within the image. In order to detect the document, an automatic document detection module at operation  1002 . Depending on the embodiment, the automatic document detection module may be specialized in detecting only certain types of documents such as financial documents (e.g. checks or deposit coupons), or may be general in detecting a variety of types of transactional documents. At the conclusion of the automatic document detection operation, the positions of the document corners are outputted (e.g. check corners) as corners A, B, C and D of quadrangle ABCD (e.g. quadrangle ABCD  502 ). Further details in regards to the automatic document detection operation will be provided with respect to  FIG. 11A . 
     Following the automatic document detection, method  1000  performs geometrical corrections to the mobile image at operation  1004 . As previously noted, this can comprise cleaning up the image by performing auto-rotate operations, de-skew operations, perspective distortion correction operations, and cropping operations. Generally, this is due to perspective distortions present in the original mobile image, as well as the possibility of incorrect orientation of the document within the mobile image. The discussion of  FIG. 15   a  will provide further detail with respect to the geometrical correction operation. 
     Next follows the binarization of the image at operation  1006 . Binarization of the image is also referred to as generating a bi-tonal image of the document at 1 bit per pixel. Binarization of the image is usually required by the Remote Deposit Systems for processing. This binarization operation will be discussed in further detail with respect to  FIGS. 16   a  and  16   c.    
     A size correction operation  1010  may be employed since many processing engines are sensitive to the image size. For example, in the context of checks, the processing engine for amount recognition may rely on the check size to distinguish personal checks from business checks, whereas the processing engine for form identification may rely on document size as an important characteristic in determining the form type. Size correction operation  1010  will be discussed in greater detail with respect to  FIG. 18 . 
     Method  1000  concludes by outputting the document as a bi-tonal image and a gray-scale image at operation  1012 . These images are subsequently utilized in processing (e.g. financial processing), depending on the type of document represented in the image. Usually, this financial processing is performed during the process completion described with respect with operation  712  of  FIG. 7 . The bi-tonal image is an image that is recognition-friendly by financial processing systems. 
     Continuing with reference to the automatic document detection operation previously described with respect to operation  1002  of  FIG. 10 ,  FIGS. 11-14  illustrate the operations of automatic document detection with greater specificity. 
     Referring now to  FIG. 11   a , a flowchart is provided illustrating a known method  1100  for automatic document detection within a color image from a mobile device. Typically, the operations described within method  1100  are performed within an automatic document detection module, however, embodiments exist where the operations reside amongst multiple modules. In addition, generally the automatic document detection module takes a variety of factors into consideration when detecting the document in the mobile image. The automatic document detection module can take into consideration arbitrary location of the document within the mobile image, the 3-D distortions within the mobile image, the unknown size of the document, the unknown color of the document, the unknown color(s) of the background, and various other characteristics of the mobile engine (e.g. resolution, dimensions, etc.). 
     Method  1100  begins at operation  1102  by receiving the original color image from the mobile device. Upon receipt, this original color image is converted into a smaller color image, also referred to as a color “icon” image, at operation  1104 . This color “icon” image preserves the color contrasts between the document and the background, while suppressing contrasts inside the document. A detailed description of the conversion process is provided with respect to  FIG. 12   a.    
     A color reduction operation is then applied to the color “icon” image at operation  1106 . During the operation, the overall color of the image is reduced, while the contrast between the document and its background is preserved within the image. Specifically, the color “icon” image of operation  1104  is converted into a gray “icon” image (also known as a gray-scale “icon” image) having the same size. The color depth reduction process is described with further detail with respect to  FIG. 13   a.    
     Subsequently, method  1100  locates the corners of the document within the gray “icon” image at operation  1108 . As previously noted in  FIG. 6 , these corners A, B, C, and D make up the quadrangle ABCD (e.g. quadrangle ABCD  502 ). Quadrangle ABCD, in turn, makes up the perimeter of the document. For example,  FIG. 11   b  depicts a check  1112  in which corners  1114  are detected by operation  1108 . Upon detection of the corners, the location of the corners is outputted at operation  1110 . 
     Referring now to  FIG. 11   c , a flowchart is provided illustrating an improved method  1101  for automatic document detection within an image from a mobile device. Method  1101  provides a faster automatic document detection that includes converting an image to a color reduced image on the mobile device and then transmitting the image to a server to perform the remaining steps of automatic document detection method  1101 . Mobile devices are often limited in terms of available processing power and transmission bandwidth. If method  1100  of  FIG. 11   a  were performed entirely on the mobile device, automatic document detection would take a substantial time on the device and occupy processor cycles of the mobile device that could be used for other tasks. Sending a color image from the mobile device to the server would also take substantial transmission time and use valuable bandwidth. It&#39;s much faster to convert to a color reduced image and transmit the color reduced image than transmitting a full color image or performing image processing on the mobile device. Improved method  1101  relies on sending a color reduced image and using a server to perform processor intensive steps of method  1101  to provide the faster image processing and faster image quality feedback from the server. Method  1101  also provides the user with the perception of faster processing since the mobile device is not occupied with the image processing operations or transmitting large amounts of data associated with color images. 
     Method  1101  begins at operation  1122  by receiving a color image from the mobile device. Upon receipt, a color reduction operation is applied to the color image at operation  1124 . During the operation, the overall color of the image is reduced resulting in a color reduced image requiring less storage size. Color reduction may be performed on a pixel-by-pixel basis using the RGB values of each pixel and a preferred weighting. Other color reduction methods may also be used, including that described in  FIG. 13   a . In some embodiments, color reduced image may be a gray-scale image. 
     Next, at operation  1126 , the color reduced image is transmitted from the mobile device to the server. Since the color depth reduction operation results in an image with a smaller size, transmission time is reduced as compared to sending a color image. Upon receipt at the server, the color reduced image is converted into a smaller color reduced image, that may be referred to as an ‘icon’ image for convenience, at operation  1128 . The conversion process involves down-scaling the color reduced image similar to the process described with respect to  FIG. 12   a.    
     Subsequently, method  1101  locates the corners of the document within the “icon” image at operation  1130 . As previously noted, the corners make up the quadrangle (e.g. quadrangle ABCD  502 ) that defines the perimeter of the document. Upon detection of the corners, the location of the corners is outputted at operation  1132 . The corner locations may be used by the server to geometrically correct the document within the color reduced image received by the server. 
     Referring now to  FIG. 12   a , a flowchart is provided describing an example method  1200  for conversion of a color image to a smaller “icon” image. The smaller “icon” image preserves the color contrasts between the document depicted therein and its background, while suppressing contrasts inside the document. Upon receipt of the original color image from the mobile device at operation  1201 , the method  1200  eliminates over-sharpening within the image at operation  1202 . Accordingly, assuming the color input image I has the dimensions of W×H pixels, operation  1202  averages the intensity of image I and downscales image I to image I′, such that image I′ has dimensions that are half that of image I (i.e. W′=W/2 and H′=H/2). Under certain embodiments, the color transformation formula can be described as the following:
 
 C ( p ′)=ave{ C ( q ): q  in  SXS -window of  p},   (eq. 5)
 
where C is any of red, green or blue components of color intensity; p′ is any arbitrary pixel on image I′ with coordinates (x′,y′); p is a corresponding pixel on image I:p=p(x,y), where x=2*x′ and y=2*y′; q is any pixel included into SXS-window centered in p; S is established experimentally; and ave is averaging over all q in the SXS-window.
 
     At the next operation  1204 , small “dark” objects within the image are eliminated. Examples of such small “dark” objects include, but are not limited to, machine-printed characters and hand-printed characters inside the document. Hence, assuming operation  1204  receives image I′ from operation  1202 , operation  1204  creates a new color image I″ referred to as an “icon” with width W″ set to a fixed small value and height H″ set to W″*(H/W), thereby preserving the original aspect ratio of image I. In some embodiments, the transformation formula can be described as the following:
 
 C ( p ″)=max{ C ( q ′): q ′ in  S′XS ′-window of p′},  (eq. 6)
 
where C is any of red, green or blue components of color intensity; p″ is an arbitrary pixel on image I″; p′ is a pixel on image I′ which corresponds to p″ under similarity transformation, as previously defined; q′ is any pixel on image I′ included into S′XS′-window centered in p′; max is maximum over all q′ in the S′XS′-window; W″ is established experimentally; S′ is established experimentally for computing the intensity I″; and I″(p″) is the intensity value defined by maximizing the intensity function I′ (p′) within the window of corresponding pixel p′ on image I′, separately for each color plane. The reason for using the “maximum” rather than “average” is to make the “icon” whiter (white pixels have a RGB-value of (255,255,255)).
 
     In the next operation  1206 , the high local contrast of “small” objects, such as lines, text, and handwriting on a document, is suppressed, while the other object edges within the “icon” are preserved. Often, these other object edges are bold. Multiple dilation and erosion operations (also known as morphological image transformations) are utilized in the suppression of the high local contrast of “small” objects. Such morphological image transformations are commonly known and used by those of ordinary skill in the art. The sequence and amount of dilation and erosion operations used is determined experimentally. Subsequent to the suppression operation  1206 , a color “icon” image is outputted at operation  1208 .  FIG. 12   b  depicts an example of the mobile image of  FIG. 11   b  after being converted into a color “icon” image. 
     Referring now to  FIG. 13   a , a flowchart is provided illustrating an example method  1300  that provides further details with respect to the color depth reduction operation  1106  as illustrated in  FIG. 11   a . At operation  1301 , the method  1300  receives a color “icon” image for color reduction. The method divides the color “icon” image into a grid (or matrix) of fixed length and width with equal size grid elements at operation  1302 . In some embodiments, the preferred grid size is such that there is a center grid element. For example, a grid size of 3×3 may be employed.  FIG. 13   b  depicts an example of the color “icon” image of  FIG. 12   b  after operation  1302  has divided it into a 3×3 grid. 
     Then, at operation  1304 , the “central part” of the icon, which is usually the center most grid element, has its color averaged. Next, the method  1300  computes the average color of the remaining parts of the icon at operation  1306 . More specifically, the grid elements “outside” the “central part” of the “icon” have their colors averaged. Usually, in instances where there is a central grid element (e.g. 3×3 grid), the “outside” of the “central part” comprises all the grid elements other than the central grid element. 
     Subsequently, method  1300  determines a linear transformation for the RGB-space at operation  1308 . The linear transformation is defined such that it maps the average color of the “central part” computed during operation  1304  to white (i.e. 255), while the average color of the “outside” computed during operation  1306  maps to black (i.e. 0). All remaining colors are linearly mapped to a shade of gray. This linear transformation, once determined, is used at operation  1310  to transform all RGB-values from the color “icon” to a gray scale “icon” image, which is then outputted at operation  1312 . Within particular embodiments, the resulting gray “icon” image (also referred to as a gray-scale “icon” image) maximizes the contrast between the document background (assuming that the document is located close to the center of the image) and the background.  FIG. 13   c  depicts an example of the color “icon” image of  FIG. 12   b  once it has been converted to a gray “icon” image. 
     Referring now to  FIG. 14 , a flowchart is provided illustrating an example method  1400  for finding document corners from a gray “icon” image containing a document. Upon receiving a gray “icon” image at operation  1401 , method proceeds to operation  1402  by finding the “voting” points on the gray “icon” image for each side of the document depicted in the image. Consequently, operation  1402  finds all positions on the gray “icon” image which could be approximated with straight line segments to represent left, top, right, and bottom sides of the document. 
     In accordance with one embodiment, operation  1402  achieves its goal by first looking for the “voting” points in the half of the “icon” that corresponds with the current side of interest. For instance, if the current side of interest is the document&#39;s top side, the upper part of the “icon” (Y&lt;H/2) is examined while the bottom part of the “icon” (Y·gtoreq·H/2) is ignored. 
     Within the selected half of the “icon,” operation  1402  would then compute the intensity gradient (contrast) in the correct direction of each pixel. This is accomplished in some embodiments by considering a small window centered in the pixel and, then, breaking the window into an expected “background” half where the gray intensity is smaller (i.e. where it is supposed to be darker) and into an expected “doc” half where the gray intensity is higher (i.e. where it is supposed to be whiter). There is a break line between the two halves, either horizontal or vertical depending on side of the document sought to be found. Next the average gray intensity in each half-window is computed, resulting in an average image intensity for the “background” and an average image intensity of the “doc.” The intensity gradient of the pixel is calculated by subtracting the average image intensity for the “background” from the average image intensity for the “doc.” 
     Eventually, those pixels with sufficient gray intensity gradient in the correct direction are marked as “voting” points for the selected side. The sufficiency of the actual gray intensity gradient threshold for determining is established experimentally. 
     Continuing with method  1400 , operation  1404  finds candidate sides (i.e. line segments) that potentially represent the sides of the document (i.e. left, top, right, and bottom sides). In order to do so, some embodiments find all subsets within the “voting” points determined in operation  1402  which could be approximated by a straight line segment (linear approximation). In many embodiments, the threshold for linear approximation is established experimentally. This subset of lines is defined as the side “candidates.” As an assurance that the set of side candidates is never empty, the gray “icon” image&#39;s corresponding top, bottom, left, and right sides are also added to the set. 
     Next, operation  1406  chooses the best candidate for each side of the document from the set of candidates selected in operation  1404 , thereby defining the position of the document within the gray “icon” image. Some embodiments use the following process in choosing the best candidate for each side of the document. 
     The process starts by selecting a quadruple of line segments {L, T, R, B}, where L is one of the candidates for the left side of the document, T is one of the candidates for the top side of the document, R is one of the candidates for the right side of the document, and B is one of the candidates for the bottom side of the document. The process then measures the following characteristics for the quadruple currently selected. 
     The amount of “voting” points is approximated and measured for all line segments for all four sides. This amount value is based on the assumption that the document&#39;s sides are linear and there is a significant color contrast along them. The larger values of this characteristic increase the overall quadruple rank. 
     The sum of all intensity gradients over all voting points of all line segments is measured. This sum value is also based on the assumption that the document&#39;s sides are linear and there is a significant color contrast along them. Again, the larger values of this characteristic increase the overall quadruple rank. 
     The total length of the segments is measured. This length value is based on the assumption that the document occupies a large portion of the image. Again, the larger values of this characteristic increase the overall quadruple rank. 
     The maximum of gaps in each corner is measured. For example, the gap in the left/top corner is defined by the distance between the uppermost point in the L-segment and the leftmost point in the T-segment. This maximum value is based on how well the side-candidates suit the assumption that the document&#39;s shape is quadrangle. The smaller values of this characteristic increase the overall quadruple rank. 
     The maximum of two angles between opposite segments (i.e. between L and R, and between T and R) is measured. This maximum value is based on how well the side-candidates suit the assumption that the document&#39;s shape is close to parallelogram. The smaller values of this characteristic increase the overall quadruple rank. 
     The deviation of the quadruple&#39;s aspect ratio from the “ideal” document aspect ratio is measured. This characteristic is applicable to documents with a known aspect ratio, e.g. checks. If the aspect ratio is unknown, this characteristic should be excluded from computing the quadruple&#39;s rank. The quadruple&#39;s aspect ratio is computed as follows: a) Find the quadrangle by intersecting the quadruple&#39;s elements; b) Find middle-point of each of the four quadrangle&#39;s sides; c) Compute distances between middle-points of opposite sides, say D1 and D2; d) Find the larger of the two ratios: R=max(D1/D2, D2/D1); e) Assuming that the “ideal” document&#39;s aspect ratio is known and Min/MaxAspectRatio represent minimum and maximum of the aspect ratio respectively, define the deviation in question as: 0, if MinAspectRatio&lt;=R&lt;=MaxAspectRatio MinAspectRatio-R, if R&lt;MinAspectRatio R-MaxAspectRatio, if R&gt;MaxAspectRatio. 
     For checks, MinAspectRatio can be set to 2.0 and MaxAspectRatio can be set to 3.0. 
     This aspect ratio value is based on the assumption that the document&#39;s shape is somewhat preserved during the perspective transformation. The smaller values of this characteristic increase the overall quadruple rank. 
     Following the measurement of the characteristics of the quadruple noted above, the quadruple characteristics are combined into a single value, called the quadruple rank, using weighted linear combination. Positive weights are assigned for the amount of “voting” points, the sum all of intensity gradients, and the total length of the segments. Negatives weights are assigned for maximum gaps in each corner, maximum two angles between opposite segments, and the deviation of the quadruple&#39;s aspect ratio. The exact values of each of the weights are established experimentally. 
     The operations set forth above are repeated for all possible combinations of side candidates, eventually leading to the “best” quadruple, which is the quadruple with the highest rank. The document&#39;s corners are defined as intersections of the “best” quadruple&#39;s sides (i.e. the best side candidates). 
     Operation  1408  subsequently defines the corners of the document using the intersections of the best side candidates. A person of ordinary skill in the art would appreciate that these corners can then be located on the original mobile image by transforming the corner locations found on the “icon” using the similarity transformation previously mentioned. Method  1400  concludes at operation  1410  by outputting the locations of the corners defined in operation  1408 . 
     With respect to the geometrical correction operation described in operation  1004  of  FIG. 10 ,  FIG. 15   a  provides a flowchart that illustrates an example method  1500  for geometric correction. As previously mentioned, geometric correction is needed to correct any possibly perspective distortions that exist in the original mobile image. Additionally, geometric correction can correct the orientation of the documentation within the original mobile image (e.g. document is orientated at 90, 180, or 270 degrees where the right-side-up orientation is 0 degrees). It should be noted that in some embodiments, the orientation of the document depends on the type of document depicted in the mobile image, as well as the fields of relevance on the document. 
     In instances where the document is in landscape orientation (90 or 270 degrees), as illustrated by the check in  FIG. 15   b , geometric correction is suitable for correcting the orientation of the document. Where the document is at 180 degree orientation, detection of the 180 degree orientation and its subsequent correction are suitable when attempting to locate an object of relevance on the document that is known to be at a specific location. For example, a MICR-line on a financial document can be one relevant object since the MICR-line is usually located at a specific location on such documents. Hence, where the financial document is a check, the MICR-line may function as the relevant object (since it is consistently located at the bottom of the check) to determine the current orientation of the check within the mobile image. In some embodiments, the object of relevance on a document depends on the document&#39;s type. For example, where the document is a contract, the object of relevance may be a notary seal, signature, or watermark positioned at a known position on the contract. Greater detail regarding correction of a document (specifically, a check) having upside-down orientation (180 degree orientation) is provided with respect to  FIGS. 17   a  and  17   c.    
     A mathematical model of projective transformations is built and converts the distorted image into a rectangle-shaped image of predefined size. For instance, where the document depicted in mobile image is a check, the predefined size is established as 1200×560 pixels, which is roughly equivalent to the dimensions of a personal check scanned at 200 DPI. 
     Continuing with reference to method  1500 , there are two separate paths of operations that are either performed sequentially or concurrently, the outputs of which are eventually utilized in the final output of method  1500 . One path of operations begins at operation  1504  where method  1500  receives the original mobile image in color. Operation  1508  then reduces the color depth of the original mobile image from a color image with 24 bit per a pixel (24 bit/pixel) to a gray-scale image with 8 bit per a pixel (8 bit/pixel). This image is subsequently outputted to operation  1516  by operation  1512 . 
     If automatic document detection method  1101  shown in  FIG. 11   c  is used, steps  1504  and  1508  may not be necessary as the server already receives a color reduced image of the original size. 
     The other path of operations begins at operation  1502 , where method  1500  receives the positions of the document&#39;s corners within the gray “icon” image produced by method  1300 . Based off the location of the corners, operation  1506  then determines the orientation of the document and corrects the orientation. In some embodiments, this operation uses the corner locations to measure the aspect ratio of the document within the original image. Subsequently, operation  1506  finds a middle-point between each set of corners, wherein each set of corners corresponds to one of the four sides of the depicted document, resulting in the left (L), top (T), right (R), and bottom (B) middle-points. The distance between the L to R middle-points and the T to B middle points are then compared to determine which of the two pairs has the larger distance. This provides operation  1506  with the orientation of the document. 
     In some instances, the correct orientation of the document depends on the type of document that is detected. For example, as illustrated in  FIG. 15   b , where the document of interest is a check, the document is determined to be in landscape orientation when the distance between the top middle-point and bottom middle-point is larger than the distance between the left middle-point and the right middle-point. The opposite might be true for other types of documents. 
     If operation  1506  determines an orientation correction is necessary, the corners of the document are shifted in a loop, clock-wise in some embodiments and counter-clockwise in other embodiments. 
     At operation  1510 , method  1500  builds the projective transformation to map the image of the document to a predefined target image size of width of W pixels and height of H pixels. In some embodiments, the projective transformation maps the corners A, B, C, and D of the document as follows: corner A to (0,0), corner B to (W,0), corner C to (W,H), and corner D to (0,H). Algorithms for building projective transformation are commonly known and used amongst those of ordinary skill in the art. 
     At operation  1516 , the projective transformation created during operation  1514  is applied to the mobile image in gray-scale as outputted from operation  1512 . The projective transformation as applied to the gray-scale image of operation  1512  results in all the pixels within the quadrangle ABCD depicted in the gray-scale image mapping to a geometrically corrected, gray-scale image of the document alone.  FIG. 15   c  is an example gray-scale image of the document depicted in  FIG. 11   b  once a geometrical correction operation is applied thereto. Method  1500  concludes at operation  1518  where the gray-scale image of the document is outputted to the next operation. 
     Now with respect to the binarization operation described in operation  1006  of  FIG. 10 , a flowchart illustrating an example method  1600  for binarization is provided in  FIG. 16   a . A binarization operation generates a bi-tonal image with color depth of 1 bit per a pixel (1 bit/pixel). In the case of documents, such as checks and deposit coupons, a bi-tonal image is required for processing by automated systems, such as Remote Deposit systems. In addition, many image processing engines require such an image as input. Method  1600  illustrates how the binarization of a gray-scale image of a document as produced by geometrical operation  1004  is achieved. This particular embodiment uses a variation of well-known Niblack&#39;s method of binarization. As such, there is an assumption that the gray-scale image received has a the dimensions W pixels×H pixels and an intensity function I(x,y) gives the intensity of a pixel at location (x,y) in terms one of 256 possible gray-shade values (8 bit/pixel). The binarization operation will convert the 256 gray-shade value to a 2 shade value (1 bit/pixel), using an intensity function B(x,y). In addition, to apply the method, a sliding window with dimensions w pixels×h pixels is defined and a threshold T for local (in-window) standard deviation of gray image intensity I(x,y) is defined. The values of w, h, and T are all experimentally determined. 
     Once method  1600  receives the gray-scale image of the document at operation  1602 , the method  1600  chooses a pixel p(x,y) within the image at operation  1604 . The method  1600  computes the average (mean) value ave and standard deviation a of the chosen pixel&#39;s intensity I(x,y) within the w×h current window location (neighborhood) of pixel p(x,y) at operation  1606 . If the standard deviation σ is determined to be too small at operation  1608  (i.e. σ&lt;T), pixel p(x,y) is considered to low-contrast and, thus, part of the background. Accordingly, at operation  1610 , low-contrast pixels are converted to white (i.e. set B(x,y) set to 1, which is white). However, if the deviation σ is determined to be larger or equal to the threshold T (i.e. σ≧T), the pixel p(x,y) is considered to be part of the foreground. In operation  1612 , if I(p)&lt;ave−k*σ, pixel p is considered to be a foreground pixel and therefore B(x,y) is set to 0 (black). Otherwise, the pixel is treated as background (and therefore B(x,y) is set to 1). In the formula above, k is an experimentally established coefficient. 
     Subsequent to the conversion of the pixel at either operation  1610  or operation  1612 , the next pixel is chosen at operation  1614 , and operation  1606  is repeated until all the gray-scale pixels (8 bit/pixel) are converted to a bi-tonal pixel (1 bit/pixel). However, if no more pixels remain to be converted  1618 , the bi-tonal image of the document is then outputted at operation  1620 .  FIG. 16   b  illustrates an example image of the check illustrated in  FIG. 15   c  subsequent to a binarization operation. 
     Now referring to  FIG. 16   c , shown is a flowchart illustrating additional operations  1601  that may be incorporated into method  1600  to provide for improved binarization of an image that may be used for further processing. Additional operations  1601  provide refined definition to the sliding window and threshold that are used in the above-described binarization process to improve the quality of the bi-tonal image within select document fields. Additional operations  1601  may be performed as part of binarization operation  1006  or part of outputting a bi-tonal image in operation  1012  of  FIG. 10 . Additional operations  1601  may be more suitable for use in operation  1012  where document fields may be more clearly defined. Additional operations  1601  provide an improved bi-tonal image where fields of the document may be later subjected to computer recognition techniques, such as, for example, OCR or hand writing recognition. 
     Additional operations  1601  are performed after choosing a pixel on the gray-scale image in operation  1604 . It is then determined whether the pixel is located within a document field in the image. A document field is an area of the document where information is expected to be located on the document. In the case of a check, for example, a rectangular area where the MICR line is expected may be a document field. Other document fields on a check may include, without limitation, the courtesy amount  102 , the legal amount  104 , date  110 , and payee  108 . If the pixel is not within a document field then, at operation  1624 , the window and threshold are selected that are used for the document as described above for processing in operation  1606 . 
     If the pixel is determined to be within a document field then the window is selected to be within the document field at operation  1626 . The size of the window may be determined experimentally but the size of the window should not extend beyond the bounds of the document field in order to avoid capturing features of the background of the document within the window. Each document field may have its own corresponding window size. By restricting the window to the document field, background artifacts outside the relevant document field will not add noise to the binarization process. After the window size is selected, the window may be positioned to place the chosen pixel near the center of the window. 
     Some embodiments may further include operation  1628  for selecting a threshold for the document field of the chosen pixel. The threshold may be experimentally determined, and each document field may have its own corresponding threshold. Selecting a threshold for a document field allows the binarization process to optimize machine processing of the information in the document fields. 
     With respect to the orientation correction operation  1008  previously described in  FIG. 10 ,  FIG. 17   a  provides a flowchart illustrating a known method for correcting the upside-down orientation of a document within an image. Specifically,  FIG. 17   a  illustrates an method  1700  for correcting the upside-down orientation of a check within a bi-tonal image.  FIG. 17   b  depicts an example bi-tonal image of a check in an upside-down orientation. A person of ordinary skill in the art would understand and appreciate that method  1700  could operate differently for other types of documents (e.g. deposit coupons). 
     As previously noted, the geometric correction operation as described in  FIG. 15  is one method for correcting a document having landscape orientation within the mobile image. However, even after the landscape orientation correction, the document still may remain in upside-down orientation. In order to the correct upside-down orientation for certain documents, some embodiments require the image containing the document be binarized beforehand. Hence, the orientation correction operation  1008  as illustrated in  FIG. 10  usually follows the binarization operation of  1006 . 
     Upon receiving the bi-tonal image of the check at operation  1702 , method  1700  reads the MICR-line at the bottom of the bi-tonal check image at operation  1704  and generates a MICR-confidence value. This MICR-confidence value (MC 1 ) is compared to a threshold value T at operation  1706  to determine whether the check is right-side-up. If MC 1 &gt;T at operation  1708 , then the bi-tonal image of the check is right side up and is outputted at operation  1710 . 
     However, if MC 1 ≦T at operation  1708 , the image is rotated 180 degrees at operation  1712 , the MICR-line at the bottom read again, and a new MICR-confidence value generated (MC 2 ). The rotation of the image by 180 degree is done by methods commonly-known in the art. The MICR-confidence value after rotation (MC 2 ) is compared to the previous MICR-confidence value (MC 1 ) plus a Delta at operation  1714  to determine if the check is now right-side-up. If MC 2 &gt;MC 1 +Delta at operation  1716 , the rotated bi-tonal image has the check right-side-up and, thus, the rotated image is outputted at operation  1718 . Otherwise, if MC 2 ≦MC 1 +Delta at operation  1716 , the original bi-tonal image of the check is right-side-up and outputted at operation  1710 . Delta is a positive value selected experimentally that reflects a higher apriori probability of the document initially being right-side-up than upside-down. 
     Now referring to  FIG. 17   c , provided is a flowchart illustrating an improved method for correcting the upside-down orientation of a document within an image. Specifically,  FIG. 17   c  illustrates a method  1701  that provides improved speed and indicates if the MICR line of the document is unreadable. Some steps of method  1701  may be performed in parallel to more quickly determine whether the image may be correctly oriented. Another improvement over method  1700  is providing an indication that the bi-tonal image cannot be correctly oriented based-on MICR line information. Since the MICR line is used for later processing (e.g. image processing for size correction and reading financial account information), an indication that the MICR line cannot be read may be used to discard the image or alert the user of the mobile device that the image is not accepted. 
     Method  1701  commences upon receiving the bi-tonal image of the check at operation  1722 . Next, the MICR line is read at the expected location of the MICR line at the bottom of the bi-tonal check image at operation  1724  and a MICR-confidence value is generated. This MICR-confidence value (MICR-Conf 1 ) is compared to a threshold value T at operation  1726  to determine if the MICR-confidence is above the threshold value. If the MICR-confidence is above the threshold it is determined that the original bi-tonal image of the check is right-side up at operation  1732  and the original bi-tonal image of the check is output. Alternatively, operation  1732  may simply provide an indication in the form of setting/clearing a flag associated with the bi-tonal image (e.g. an upside-down flag may be cleared in operation  1732  to indicate that the image is correctly oriented). 
     At operation  1728 , the bi-tonal image is rotated 180 degrees, the MICR line at the bottom of the check is read again, and a new MICR-confidence value is generated (MICR-Conf 2 ). The MICR-confidence value after rotation (MICR-Conf 2 ) is compared to the threshold value T at operation  1730  to determine if the MICR-confidence is above the threshold value. If the MICR-confidence is above the threshold it is determined that the original bi-tonal image of the check is upside-down at operation  1734  and the 180 degree rotated bi-tonal image of the check is output. Alternatively, operation  1734  may simply provide an indication in the form of setting/clearing a flag associated with the bi-tonal image (e.g. an upside-down flag may be set in operation  1734  to indicate that the bi-tonal image is upside-down). 
     Operations  1724  and  1726  may be performed in parallel with operations  1728  and  1730  in order to quickly determine whether both MICR confidence readings are below the threshold T. By performing both operations in parallel, the mobile device may be provided with faster feedback from the server to indicate the suitability of the images provided from the mobile device. In addition, by comparing the MICR confidence to a threshold in both orientations, false positives may be avoided where it is unknown if the image is correctly oriented. 
     If the MICR confidence of the original and rotated images are both not above the threshold, it is indicated that the orientation of the image is unknown at operation  1736 . A flag may be set that is associated with the bi-tonal image to indicate that orientation is unknown. In some embodiments, the bi-tonal image may then be provided to an alternative orientation correction module that relies on another feature of the document to correct orientation. If the image is expected or required to have a legible MICR line, the image may be discarded and the mobile device can be alerted that the image is not acceptable. 
     An alternative sequential embodiment is also shown in  FIG. 17   c  as path  1740  to operation  1728 . In this embodiment, operation  1728  performed on the rotated image is only performed if the MICR confidence of the original image is not above the threshold. If the MICR Confidence of the original bi-tonal image is not above the threshold, the sequential embodiment may proceed along “No” path  1740  of the flowchart to operation  1728  where the bi-tonal image is then rotated and the second MICR confidence is generated. 
     Other embodiments may rely on the number of MICR characters that can be read as a form of MICR confidence. For example, at operation  1724  it could be determined how many MICR characters were read, and then at operation  1726  the threshold can be number of MICR characters that are expected, which is then compared with the actual number of MICR characters read. 
     With respect to the size correction operation  1010  illustrated in  FIG. 10 ,  FIGS. 18   a  and  18   b  provides a flowchart illustrating an example method for size correction of an image. Specifically,  FIGS. 18   a  and  18   b  illustrate example methods  1800 ,  1801  for correcting the size of a check within a bi-tonal image, where the check is oriented right-side-up. A person of ordinary skill in the art would understand and appreciate that methods  1800 ,  1801  could operate differently for other types of documents (e.g. deposit coupons). 
     Since many image processing engines are sensitive to image size, it is crucial that the size of the document image be corrected before it can be properly processed. For example, a form identification engine may rely on the document size as an important characteristic for identifying the type of document that is being processed. Generally, for financial documents such as checks, the image size should be equivalent to the image size produced by a standard scanner running at 200 DPI. 
     In addition, where the document is a check, during the geometric correction operation the geometrically corrected predefined image size is at 1200×560 pixels (See, for e.g.,  FIG. 15  description), which is roughly equivalent to the size of a personal check scanned at 200 DPI. However, the size of business checks tend to vary significantly, with most business checks having a width greater than 1200 pixels when scanned at 200 DPI. Some business checks are known to be as wide as 8.75″, which translates to be 1750 pixels in width when scanned at 200 DPI. Hence, in order to restore the size of business checks and other check variations that have been geometrically corrected at a predefined image size of 1200×560 pixels, the size correction operation is performed. 
     Referring now to  FIG. 18   a , after receiving a bi-tonal image containing a check that is orientated right-side-up at operation  1802 , method  1800  reads the MICR-line at the bottom of the check at operation  1804 . This allows method  1800  to compute the average height of the MICR-characters at operation  1806 . In doing so, the computed average height gets compared to the height of a MICR-character at 200 DPI at operation  1808 , and a scaling factor is computed accordingly. The scaling factor SF is computed as follows:
 
SF=H200/AH,  (eq. 7)
 
where AH is the average height of the MICR-characters found; and H200 is the corresponding “theoretical” height value based on the ANSI x9.37 standard (Specifications for Electronic Exchange of Check and Image Data) at 200 DPI.
 
     Method  1800  uses the scaling factor at operation  1810  to determine whether the bi-tonal image of the check requires size correction. If the scaling SF is determined to be less than or equal to 1.0+Delta, then method  1800  outputs the most recent versions of the check&#39;s bi-tonal image and the check&#39;s the gray-scale image at operation  1812 . Delta defines the system&#39;s tolerance to wrong image size. 
     If, however, the scaling factor SF is determined to be higher than 1.0+Delta, then at operation  1811  the preliminary dimensions of the check are computed as follows:
 
AR=HS/WS  (eq. 8)
 
H′=H*SF  (eq. 9)
 
W′=H′/AR,  (eq. 10)
 
where HS and WS are the height and width of the check snippet found on the original image; AR is the check aspect ratio which we want to maintain while changing the size; W is the width of geometrically corrected image before it&#39;s size is adjusted; W′ is the preliminary adjusted check&#39;s width in pixels; and preliminary H′ is the adjusted check&#39;s height in pixels.
 
     The preliminary height and width (H′ and W′) are then compared to the nearest check known check dimensions at 200 DPI to adjust the scaling factor. For example, if H′ and W′ are calculated to correspond to 2.48″ by 4.92″, these dimensions are closest to a known check measurement of 2.5″ by 5″. Since many check dimensions are multiples of ⅛th of an inch, alternative embodiments may simply round the expected dimensions of the check to the nearest ⅛th of an inch to determine the nearest known check dimensions. The scaling factor may then be adjusted as follows:
 
AFH=HNK/H′  (eq. 11)
 
AFW=WNK/W′  (eq. 12)
 
H″=AFH*H′  (eq. 13)
 
W″=AFW*W′  (eq. 14)
 
where AFH and AFW are the adjustment factors applied to the height and width respectively; HNK and WNK is the nearest known height and width, respectively; H″ is the final adjusted check&#39;s height in pixels; and W″ is the final adjusted check&#39;s width in pixels. Since separate adjustment factors are applied to the height and width, small errors in the aspect ratio of the image may be corrected.
 
     Subsequent to re-computing the final dimensions, operation  1814  repeats geometrical correction and binarization using the newly dimensioned check image. Following the repeated operations, operation  1812  outputs the resulting bi-tonal image of the check and gray-scale image of the check. 
     Referring now to  FIG. 18   b , shown is a flowchart illustrating an example method  1801  for size correction of an image that relies on using both height and width measurements to scale the image. Operations that correspond to operations shown in  FIG. 18   a  are similarly numbered. Method  1801  provides for improved scaling when the aspect ratio of the geometrically corrected image does not correspond to the original document. Rather than relying on the aspect ratio of the image to perform size correction, both height and width scaling factor are computed. 
     At operation  1807 , the average height and width of the MICR characters are calculated. The computed average height and width get compared to the size of a MICR character at 200 DPI at operation  1809 , and a scaling factor is computed accordingly. The scaling factors are computed as follows:
 
SFH=H200/AH  (eq. 7)
 
and
 
SFW=W200/AW  (eq. 15)
 
where SFH and SFW are the scaling factors to be applied to the height and width respectively; AW is the average width of the MICR-characters found; and W200 is the corresponding “theoretical” width value based on the ANSI x9.37 standard at 200 DPI. The final height and width may then be calculated independent of each other (i.e. not related by aspect ratio) as follows:
 
H″=SFH*H  (eq. 16)
 
W″=SFW*W  (eq. 17).
 
     As an alternative to computing the average width at operations  1806  and  1807 , improved accuracy may be obtained by using distances relative to MICR symbols, such as, for example, the transit symbol  113  or on-us symbol  115  shown in  FIG. 1 . Checks use standard distances between certain MICR symbols or between MICR symbols and the leading edge (right edge of front facing check) or bottom edge of the document. A scaling factor can be computed using the theoretical distance based on the standard and measured distance in the image. Since these distances are larger than the width of a MICR character, scaling using distance relative to MICR symbols is less prone to error, and thus more accurate. 
     As an example, at operation  1804 , the MICR line is read to determine the distance between two transit symbols. The scaling factor SF is then computed, at operations  1808  or  1809 , as follows:
 
SF=TDist200/MD,  (eq. 18)
 
where MD is the measured distance between the transit symbols; and TDist200 is the corresponding distance according to the check MICR standard at 200 DPI. The scaling factor may be calculated similarly using the measured distance from the leading edge of the check to the first transit symbol and the distance according to the standard at 200 DPI. Some embodiments may use a number of measurements on the check image to compute a number of scaling factors that may then be averaged to compute the scaling factor to apply to the image. Some embodiments may only select only one of a window or threshold, while other embodiments may select both a window and threshold.
 
     As used herein, the term module might describe a given unit of functionality that can be performed. As used herein, a module might be implemented utilizing any form of hardware, software, or a combination thereof. For example, one or more processors, controllers, ASICs, PLAs, logical components, software routines or other mechanisms might be implemented to make up a module. In implementation, the various modules described herein might be implemented as discrete modules or the functions and features described can be shared in part or in total among one or more modules. In other words, as would be apparent to one of ordinary skill in the art after reading this description, the various features and functionality described herein may be implemented in any given application and can be implemented in one or more separate or shared modules in various combinations and permutations. Even though various features or elements of functionality may be individually described or claimed as separate modules, one of ordinary skill in the art will understand that these features and functionality can be shared among one or more common software and hardware elements, and such description shall not require or imply that separate hardware or software components are used to implement such features or functionality. 
     Where components or modules of processes used in conjunction with the operations described herein are implemented in whole or in part using software, in one embodiment, these software elements can be implemented to operate with a computing or processing module capable of carrying out the functionality described with respect thereto. One such example-computing module is shown in  FIG. 19 . Various embodiments are described in terms of this example-computing module  1900 . After reading this description, it will become apparent to a person skilled in the relevant art how to implement the invention using other computing modules or architectures. 
     Referring now to  FIG. 19 , computing module  1900  may represent, for example, computing or processing capabilities found within desktop, laptop and notebook computers; mainframes, supercomputers, workstations or servers; or any other type of special-purpose or general-purpose computing devices as may be desirable or appropriate for a given application or environment. Computing module  1900  might also represent computing capabilities embedded within or otherwise available to a given device. For example, a computing module might be found in other electronic devices. Computing module  1900  might include, for example, one or more processors or processing devices, such as a processor  1904 . Processor  1904  might be implemented using a general-purpose or special-purpose processing engine such as, for example, a microprocessor, controller, or other control logic. 
     Computing module  1900  might also include one or more memory modules, referred to as main memory  1908 . For example, random access memory (RAM) or other dynamic memory might be used for storing information and instructions to be executed by processor  1904 . Main memory  1908  might also be used for storing temporary variables or other intermediate information during execution of instructions by processor  1904 . Computing module  1900  might likewise include a read only memory (“ROM”) or other static storage device coupled to bus  1902  for storing static information and instructions for processor  1904 . 
     The computing module  1900  might also include one or more various forms of information storage mechanism  1910 , which might include, for example, a media drive  1912  and a storage unit interface  1920 . The media drive  1912  might include a drive or other mechanism to support fixed or removable storage media  1914 . For example, a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. Accordingly, storage media  1914  might include, for example, a hard disk, a floppy disk, magnetic tape, cartridge, optical disk, a CD or DVD, or other fixed or removable medium that is read by, written to or accessed by media drive  1912 . As these examples illustrate, the storage media  1914  can include a computer usable storage medium having stored therein particular computer software or data. 
     In alternative embodiments, information storage mechanism  1910  might include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing module  1900 . Such instrumentalities might include, for example, a fixed or removable storage unit  1922  and an interface  1920 . Examples of such storage units  1922  and interfaces  1920  can include a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, a PCMCIA slot and card, and other fixed or removable storage units  1922  and interfaces  1920  that allow software and data to be transferred from the storage unit  1922  to computing module  1900 . 
     Computing module  1900  might also include a communications interface  1924 . Communications interface  1924  might be used to allow software and data to be transferred between computing module  1900  and external devices. Examples of communications interface  1924  might include a modem or softmodem, a network interface (such as an Ethernet, network interface card, WiMedia, IEEE 802.XX (or other interface), a communications port (such as for example, a USB port, IR port, RS232 port Bluetooth® interface, or other port), or other communications interface. Software and data transferred via communications interface  1924  might typically be carried on signals, which can be electronic, electromagnetic (which includes optical) or other signals capable of being exchanged by a given communications interface  1924 . These signals might be provided to communications interface  1924  via a channel  1928 . This channel  1928  might carry signals and might be implemented using a wired or wireless communication medium. These signals can deliver the software and data from memory or other storage medium in one computing system to memory or other storage medium in computing system  1900 . Some examples of a channel might include a phone line, a cellular link, an RF link, an optical link, a network interface, a local or wide area network, and other wired or wireless communications channels 
     Computing module  1900  might also include a communications interface  1924 . Communications interface  1924  might be used to allow software and data to be transferred between computing module  1900  and external devices. Examples of communications interface  1924  might include a modem or softmodem, a network interface (such as an Ethernet, network interface card, WiMAX, 802.XX or other interface), a communications port (such as for example, a USB port, IR port, RS232 port, Bluetooth interface, or other port), or other communications interface. Software and data transferred via communications interface  1924  might typically be carried on signals, which can be electronic, electromagnetic, optical or other signals capable of being exchanged by a given communications interface  1924 . These signals might be provided to communications interface  1924  via a channel  1928 . This channel  1928  might carry signals and might be implemented using a wired or wireless medium. Some examples of a channel might include a phone line, a cellular link, an RF link, an optical link, a network interface, a local or wide area network, and other wired or wireless communications channels. 
     In this document, the terms “computer program medium” and “computer usable medium” are used to generally refer to physical storage media such as, for example, memory  1908 , storage unit  1920 , and media  1914 . These and other various forms of computer program media or computer usable media may be involved in storing one or more sequences of one or more instructions to a processing device for execution. Such instructions embodied on the medium, are generally referred to as “computer program code” or a “computer program product” (which may be grouped in the form of computer programs or other groupings). When executed, such instructions might enable the computing module  1900  to perform features or functions as discussed herein. 
     While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not of limitation. The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments. Where this document refers to technologies that would be apparent or known to one of ordinary skill in the art, such technologies encompass those apparent or known to the skilled artisan now or at any time in the future. In addition, the invention is not restricted to the illustrated example architectures or configurations, but the desired features can be implemented using a variety of alternative architectures and configurations. As will become apparent to one of ordinary skill in the art after reading this document, the illustrated embodiments and their various alternatives can be implemented without confinement to the illustrated example. One of ordinary skill in the art would also understand how alternative functional, logical or physical partitioning and configurations could be utilized to implement the desired features. 
     Furthermore, although items, elements or components may be described or claimed in the singular, the plural is contemplated to be within the scope thereof unless limitation to the singular is explicitly stated. The presence of broadening words and phrases such as “one or more,” “at least,” “but not limited to” or other like phrases in some instances shall not be read to mean that the narrower case is intended or required in instances where such broadening phrases may be absent. While the exemplary embodiments have been described herein, it is to be understood that the invention is not limited to the disclosed embodiments. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims, and scope of the claims is to be accorded an interpretation that encompasses all such modifications and equivalent structures and functions.