Abstract:
A system and method for detecting the quality of a captured digital image depicting a hardcopy document are disclosed. The captured digital image is analyzed to determine a corresponding blurriness, noise, hotspot, and uneven illumination metric representing a quality level of the image data. The blurriness, noise, hotspot, and uneven illumination metrics are then combined to formulate a pass/caution/fail indicator for the user to respond to the captured digital image quality.

Description:
FIELD OF THE INVENTION 
     This invention pertains to the field of digital imaging, and more particularly to a method for evaluating the quality of a document within a digital image captured on a mobile device or camera. 
     BACKGROUND OF THE INVENTION 
     Digital cameras offer significant advantages over scanners for capturing document images and other images. Digital cameras are generally more portable than scanners, allowing users to image documents wherever they are and as the need arises rather than having to bring the document to a scanner. In addition, because scanners require that the document to be digitized be placed on a scanner platen, digital cameras are capable of capturing images of a wider variety of document types (e.g., larger documents or documents mounted in a frame) than scanners. However, the use of digital cameras creates difficulties that do not exist when using a scanner. For example, lighting conditions vary when using a digital camera, whereas the lighting conditions are generally controlled for scanners. In addition, use of a digital camera can introduce geometric distortions depending on various factors such as the angle and distance of the digital camera relative to the document, the lens used by the digital camera, and whether the document is situated on a flat or curved surface. Because scanners generally utilize a moving scanner head, at a fixed distance from a document which is held flat on the platen, these distortions do not typically occur in scanners. Another difficulty in capturing images of documents with a digital camera is the wide variety of different possible background shapes, patterns and colors. 
     Digital cameras (in particular, those included in mobile phones, smartphones, tablets, and other mobile devices) typically have a relatively small preview display as part of the device. This small screen makes it difficult for a user to assess the quality of the captured document by using the preview screen. Although the device display in many smartphones also acts as the preview or display screen for captured images, these screens may still be too small for a user to determine whether an image of a document is sufficient for the desired post-processing through visual inspection. 
     A typical application applied to scanned documents is using optical character recognition (OCR) to extract ASCII data for use later, such as indexing the document. Blurriness, noise, and uneven illumination will have a negative effect on the ability to OCR a document capture with a digital camera. Another problem is that flash use during image capture with a digital camera can cause a hotspot problem where part of the captured image data is obscured due to the flash. What is needed is a way to measure and analyze blurriness, noise, hotspot, and uneven illumination at the time of capture so the user can be alerted to re-capture the document with the digital camera. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to a system and method for measuring the quality of a captured digital image of a document, and in particular to measuring the quality of documents captured with a digital camera and generating an alert or instruction to re-capture the image of the document if it is not sufficient for desired further processing, such as OCR recognition. The digital camera may be included in a smartphone, tablet, or other portable device. When included in a digital smartphone, tablet, or other portable device, the system and method may be implemented as part of an application program executed by the device. The application program may be programmed to control functionality of the camera. The application may then analyze any images captured, as described in more detail below, and alert the user if the captured image of the document is insufficient for desired later processing. This same functionality may be provided within programming on a conventional digital camera, either as a standard feature or a selectable option. Alternatively, the processing and analysis could be done on a separate computing device that receives the image, either through a networked cloud computing environment or direct transfer from the camera to a memory associated with the device. 
     In addition to analyzing documents, the system and method described herein may be used to detect the same types of defects in conventional photographs taken with a digital camera. Similar alerts could be provided to suggest that the user re-take the photograph. 
     Using the digital camera or other digital imaging system, a digital image is captured that depicts a hardcopy document on a background. The digital image may be color, black and white, or grayscale. When capturing a color image, the color digital image may include image data for a plurality of color channels, which are analyzed to determine a quality metric, representing a measure of the ability to extract information from the image data. The image data for the plurality of color channels is then converted to a gray scale equivalent. When the captured image is grayscale or black and white, conversion may not be necessary, although these images are also analyzed to determine the quality metric. The processor within the digital camera, or mobile device including a digital camera, further analyzes the image data for the metrics of blurriness, noise, uneven illumination, and hotspot to detect the quality of the depicted hardcopy document. After the metrics have been determined, the processor stores an indication of the document quality metrics in a processor-accessible memory. The quality measurements are analyzed by the processor to insure a usable document image was obtained at the time of capture that can then be used to extract information using optical character recognition. 
     If the processing determines that the image is not of sufficient quality, an alert can be provided to the operator indicating that the document should be re-captured. This process can be repeated until an image of the document is obtained that is determined to be of sufficient quality to perform the desired post-capture processing on the image, such as OCR recognition. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level diagram showing the components of a system for processing a captured digital image. 
         FIG. 2  is a flowchart showing a method of processing the captured digital images and determining the document image quality. 
         FIG. 3  is a flowchart showing a method of processing the captured digital images and converting them to resized gray scale image. 
         FIG. 4  depicts the details of the processing step of determining the presence of a hotspot in the method of  FIG. 2 . 
         FIG. 5  depicts the details of the processing step of determining the presence of noise in the method of  FIG. 2 . 
         FIG. 6  depicts the details of the processing determining the presence of blurriness in the method of  FIG. 2 . 
         FIG. 7  depicts the details of the processing of determining the presence of uneven illumination in the method of  FIG. 2 . 
         FIG. 8  depicts an additional processing step that uses the metrics (hotspot, blurriness, noise, and uneven illumination) to create a STOP/CAUTION/GO indicator. 
         FIG. 9  depicts the processing steps that are used to calculate the hotspot metric in the method of  FIG. 2 . 
         FIG. 10  depicts the processing steps that are used to calculate the noise metric in the method of  FIG. 2 . 
         FIG. 11  depicts the processing steps that are used to calculate the blurriness metric in the method of  FIG. 2 . 
         FIG. 12  depicts an example of an image with uneven illumination, and shows the processing steps that are used to calculate the uneven metric in the method of  FIG. 2 . 
         FIG. 13  illustrates an example of uneven illumination and the creation of a residual image by adding a fraction of a mean-blurred image plane to the stored gray scale image plane. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a high-level diagram showing the components of a digital camera system for capturing and processing images. The system includes a data processing system  110 , a peripheral system  120 , a user interface system  130 , and a data storage system  140 . The peripheral system  120 , the user interface system  130  and the data storage system  140  are communicatively connected to the data processing system  110 . These systems may be included within a digital camera, or within a mobile device that contains a digital camera, such as a smartphone, tablet, or PDA. Alternatively, the images from a digital camera may be transmitted to a separate system for processing. For example, the digital camera may transmit images to a server on a cloud computing network. The server may process the images, and transmit a determination of image quality back to the digital camera. 
     The data processing system  110  includes one or more data processing devices that implement the processes of the various embodiments of the present invention, including the example processes described herein. The data processing devices may be, for example, a central processing unit (“CPU”), a desktop computer, a laptop computer, a mainframe computer, a personal digital assistant, a smartphone, a tablet, a digital camera, cellular phone, or any other device for processing data, managing data, or handling data. The processor system  110  may also include an OCR engine for converting the captured image of the document to text. 
     The data storage system  140  includes one or more processor-accessible memories configured to store information, including software instructions executed by the processor and captured image data. The data storage system  140  may be a distributed processor-accessible memory system including multiple processor-accessible memories communicatively connected to the data processing system  110  via a plurality of computers or devices. On the other hand, the data storage system  140  need not be a distributed processor-accessible memory system and, consequently, may include one or more processor-accessible memories located within a single data processor or device. 
     The processor-accessible memory may be any processor-accessible data storage device, whether volatile or nonvolatile, electronic, magnetic, optical, or otherwise, including but not limited to, registers, floppy disks, hard disks, Compact Discs, DVDs, flash memories, ROMs, and RAMs. 
     The system components may be communicatively connected in any manner that enables transmissions of data between components, including wired or wireless transmissions between devices, data processors, or programs in which data may be communicated. This connection may include a connection between devices or programs within a single data processor, a connection between devices or programs located in different data processors, and a connection between devices not located in data processors at all. In this regard, although the data storage system  140  is shown separately from the data processing system  110 , the data storage system  140  may be stored completely or partially within the data processing system  110 . Further in this regard, although the peripheral system  120  and the user interface system  130  are shown separately from the data processing system  110 , one or both of such systems may be stored completely or partially within the data processing system  110 . 
     The peripheral system  120  may include one or more devices configured to provide digital content records to the data processing system  110 . For example, the peripheral system  120  may include digital still cameras, digital video cameras, cellular phones, or other data processors. The data processing system  110 , upon receipt of digital content records from a device in the peripheral system  120 , may store such digital content records in the data storage system  140 . The peripheral system  120  does not need to be external to the digital camera device that includes the data processing system  110 , user interface system  130 , and data storage system  140 . For example, the peripheral system could be the camera components within a smartphone, or the digital video capture component&#39;s in a conventional digital camera. 
     The user interface system  130  may include a touch screen, touch pad, keypad, mouse, keyboard, another computer, or any device or combination of devices from which data is input to the data processing system  110 . In this regard, and as noted above, although the peripheral system  120  is shown separately from the user interface system  130 , the peripheral system  120  may be included as part of the user interface system  130 . The user interface system  130  also may include a display device, a processor-accessible memory, or any device or combination of devices to which data is output by the data processing system  110 . In this regard, if the user interface system  130  includes a processor-accessible memory, such memory may be part of the data storage system  140  even though the user interface system  130  and the data storage system  140  are shown separately in  FIG. 1 . 
     As shown in  FIG. 2 , a digital image depicting a scene that includes a document is captured  200  using a digital imaging system such as a digital camera or a camera phone. The captured image of the document may be of any image depicted on an appropriate hardcopy of softcopy medium. For example, the documents may be reports, letters, receipts, invoices, magazines, books, newspapers, photographic prints, artwork (e.g., paintings), or content displayed on a television or computer display. The documents can include various types of content including text, graphics and photographic images. Most documents will generally have a rectangular boundary, although the boundary may be geometrically distorted according to the perspective of the digital imaging system and the flatness of the document when the image was captured. 
     The captured digital image  200  includes an array of image pixels having pixel values. The captured digital image  200  may be analyzed  205  by the system processor to determine if it is a color image having a plurality of color channels (e.g., red (R), green (G) and blue (B) color channels). 
     A number of preprocessing operations  210  including converting the color image to a gray scale image representation are applied to the captured digital image  200  to provide a processed digital image in preparation for further processing.  FIG. 3  shows an example of preprocessing operations  210  performed following application of the analysis step  205  in  FIG. 2  on a received captured digital image  200  (in an RGB color space) that depicts a document to convert from the RGB color space to a single channel gray scale. The preprocessing operations  210  may include a resize digital image to a longest side of 1024 step  305 , a Gaussian blur step  310 , a conversion from a color space to a gray scale space image step  315 . When the captured image is already in grayscale or black and white, this conversion process may not be necessary. The resize digital image step  305  is used to reduce the size of the captured digital image  200  in order to reduce the memory requirements and increase the computational efficiency of the remaining steps. In systems with advanced processor, this reduction step may not be necessary if the processing and memory components are sufficient to handle the full sized images. The reduced size should be large enough to enable an accurate detection of the document boundary. For example, the resize digital image step  305  may reduce the size of the captured digital image so that the largest dimension of the image is about 1000 pixels (e.g., 1024 pixels). Alternatively, the image may simply be resized by a fixed resize factor (e.g., 25%). Image resizing methods typically involve applying interpolation processes such as bilinear, bicubic or nearest-neighbor interpolation. The captured digital image  200  can optionally be convolved with an appropriate anti-aliasing filter before the interpolation process is applied to reduce the formation of aliasing artifacts (e.g., “jaggies”) during the resizing process. 
     The color space conversion step  315  performs a color space conversion to convert the image data to an appropriate gray scale space  315 . This may be done, for example, by converting the image date to the GRAY color space, which is a color space having a single gray (G) color channel. The “gray” color channel is related to the luminance (or lightness) of the image. Using the GRAY color space has the desirable characteristic that the channel relates to overall luminance and does not rely on color information. After the RGB to gray scale conversion  315  is completed, a region of interest is taken  317  that is used for subsequent analysis. The region of interest may be a center region of interest, the whole image, or any other desired portion of the image to be analyzed. In particular, a center region of interest may be taken by cropping off a certain percentage of the edges of the image, thereby reducing the height and width of the image that will be processed. The region of interest is then stored as a gray scale image at  320  for further processing. Since the document being captured is likely to be near the center of the captured digital image  200 , the information in proximity to the edges of the image may not necessary and by discarding this information, processing will be faster. Before discarding the information from the edge of the documents, the processor may first perform a check to determine if the edges of the image appear to contain text or other features that would be of interest in subsequent processing as described below. The stored gray scale image  320  may be a plane of two-dimensional pixels making up the image, with each pixel having a value related to luminescence (or brightness). 
     Returning to  FIG. 2 , after the region of interest has been stored as a gray scale image  320  to be used for subsequent processing in step  210  (as detailed in  FIG. 3 ), the method then implements steps to check for certain defects in the captured image. These include a hotspot determination  220 , a noise determination  225 , an uneven illumination determination  230 , and a blurriness determination  235 . 
     A hotspot determination  220  may be performed to detect the presence of a hotspot in the image. A hotspot is a high level pixel grouping in the image that usually leads to a loss of information in the region. If there is a loss of information in the captured document, the extraction of the information becomes impossible for the OCR engine and the captured image could not be used to extract the text of the document. 
     The hotspot determination step  220  is used to measure whether a hotspot is present in the stored gray scale image  320  from preprocessing  210 .  FIG. 4  illustrates a detailed representation of the steps involved in the hotspot processing  220  in  FIG. 2 . Several statistics of the image information are computed for use in a machine learning classifier, including minimum value, maximum value, mean value, and standard deviation. The pixel with the minimum value in the plane of pixels  405  making up the gray scale image is calculated by iteratively comparing the value of each pixel within the plane. The value of the first pixel examined is initially stored as the minimum value. Then the system iteratively checks the value of every other pixel within the system. If a checked pixel value is less than the currently stored minimum, then the stored minimum is replaced with the pixel value. As shown below, the process of determining the minimum pixel value in the image may begin by setting stored minimum of infinity. Then, a first pixel in the gray scale image would be checked, and since its value would necessarily be less than infinity, its value replaces “infinity” as the currently stored minimum. Then, the system then iteratively checks the value each pixel in the image, and compares it to the currently stored minimum. If the value of a pixel is less than the currently stored minimum, it becomes the new currently stored minimum that all remaining pixels are compared to in the iterative process. Once the values of all pixels have been checked, the final stored minimum value pixel is determined to be the minimum value for the image. 
     Minimum Identification Process: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                   
                 min=infinity 
               
               
                   
                   
                 for each value in gray scale image 
               
               
                   
                   
                  if value &lt; min 
               
               
                   
                   
                   min = value 
               
               
                   
                   
               
             
          
         
       
     
     Similarly, a maximum value of the plane  410  is calculated by iteratively comparing values of each pixel in the image. As with the determination of a minimum value, the system checks a first pixel and sets its value as the currently stored maximum value. Then, a second pixel is checked and compared against the value of the first pixel, and the second pixel value becomes the currently stored maximum if it is greater. If it is less than the currently stored maximum, the stored value does not change and the next pixel checked is again compared against the stored value of the first pixel. The pixels in the image are iteratively analyzed, and the currently stored maximum value is updated each time a pixel checked has a greater value than the stored value. If the value of a pixel is greater than the currently stored maximum, it becomes the new currently stored maximum that all remaining pixels are compared to in the iterative process. Once the values of all pixels have been checked, the final stored maximum value pixel is determined to be the maximum value for the image. 
     Maximum Identification Process: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                   
                 max=0 
               
               
                   
                   
                 for each value in gray scale image 
               
               
                   
                   
                  if value &gt; max 
               
               
                   
                   
                   max = value 
               
               
                   
                   
               
             
          
         
       
     
     The mean value of the plane  415  is calculated by summing each pixel value and, when all pixel values have been summed, the resulting sum is divided by the number of pixel values in the entire image. The result is the mean or average value of the plane  415 . 
     Mean Identification Process: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                   
                 sum=0 
               
               
                   
                   
                 for each value in gray scale image 
               
               
                   
                   
                  sum = sum + value 
               
               
                   
                   
                 end 
               
               
                   
                   
                 mean = sum / num of values in gray scale image 
               
               
                   
                   
               
             
          
         
       
     
     The standard deviation value of the plane  420  is calculated by first determining, for each pixel in the image plane, the difference between the pixel value and the mean value of the plane  415 , and squaring each result. Then, the square root of the mean value of the calculated differences is determined, providing a standard deviation value of the plane  420 . 
     Standard Deviation Identification Process: 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                   
                 sum=0 
               
               
                   
                 for each value in gray scale image 
               
               
                   
                  sum = squared(value − mean) 
               
               
                   
                 end 
               
               
                   
                 stdDev = squareRoot(sum / (num of values in gray scale image − 1)) 
               
               
                   
               
             
          
         
       
     
     Referring back to  FIG. 4  the values for minimum, maximum, mean, and standard deviation are used to calculate the hotspot value by applying a machine learning tree. The tree is a decision tree that is a series of comparisons of an input value versus a preset value that is determined via a training method. The training programs can be implemented in programs such as Matlab, R statistical programs, or any other commercially developed or open source programs. The training process begins by capturing a set of images that are known to contain blurriness, hotspots, noise, and/or uneven illumination. Another set of images is captured that do not contain any blurriness, hotspots, noise, or uneven illumination. The first set is considered the failure set and the second set is considered the success set. Next, the blurriness, hotspots, noise, and/or uneven illumination values for all the images are measured and the images are labeled either via tagging or in a separate file such as a spreadsheet containing no defects, blurriness, hotspots, noise, and/or uneven illumination. All the images are also labelled with either a pass value or a fail value, based on whether the extent of the defects renders the image unusable for further processing. 
     The training of the machine learning tree may be done using Classification and regression trees, commonly abbreviated as CART. By presenting a series of images that contain the values for blurriness, hotspots, noise, or uneven illumination and also presenting a series of images that do not contain any problems, in addition to the pass/fail value, the machine learns the appropriate patterns and generates a tree that is used to classify an unknown image value. 
       FIG. 9  is contains an example of an image with a hotspot  900 . A decision tree has been determined using an R statistical modeling program and the tree is represented in  910 . As indicated above, other programs such as a neural network, support vector machine, S Plus, or MatLab may be used to establish the decision tree to detect hotspots. In the decision tree  910 , a determination is first made as to whether the maximum pixel value in the image is greater than or equal to a predetermined threshold value. 
     If the maximum value in the image is greater than the threshold value, a determination is made as to whether the mean pixel value is greater than or equal to a predetermined mean threshold value. If not, it is determined that a hotspot is present and a “fail” is indicated for the image. If the mean pixel value is greater than or equal to the threshold value, a further determination is made as to whether the standard deviation is greater than or equal to a predetermined threshold value. If it is, it is determined that the image does not contain a hotspot and a “pass” is indicated. If it is not, it is determined that a hotspot is present and a “fail” is indicated. 
     When the maximum pixel value in the image is less than the threshold, a determination is made as to whether the minimum pixel value in the image is greater than or equal to a predetermined minimum threshold. If it is, it is determined that the image does not contain a hotspot and a “pass” is indicated. If it is not, it is determined that a hotspot is present and a “fail” is indicated. When the output of the decision tree  910  is a “pass,” the hotspot condition for image is considered to be true. When the output of the decision tree  910  is a “fail,” the hotspot condition for the image is considered to be false. As shown in  FIG. 4 , in the event that the hotspot decision tree returns false  430 , the hotspot flag is stored as false  440 . In the event that the hotspot decision tree returns true  435 , the hotspot flag is stored as true  440 . 
     Referring back to  FIG. 2 , another processing step that may be performed on the stored gray scale image  320  is a determination of the presence of noise  225 .  FIG. 5  illustrates the details in the processing step of determining the presence of noise  225  in  FIG. 2 . As seen in  FIG. 5 , at  505  a median blur is applied to the gray scale image plane, creating a signal plane. This median blur may be applied using a median filter, such as a 3×3 median filter. At step  510 , the signal plane is subtracted from the gray scale image plane, creating a residual noise plane. A percentile of the noise in the residual noise plane, such as 90%, is then calculated at  515 . At step  520 , a threshold percentile of the signal in the gray scale image is determined, and at  525  signal and noise values for the image are determined. A machine learning noise tree is applied at  526  to determine if there is excessive noise in the image. If the noise value is greater than a predetermined noise threshold value and the signal value is greater than a predetermined signal threshold value, then the image contains excessive noise.] A check is performed to determine if the image contains noise at  527 . If it is determined that noise is present, a noise flag is set as true at  535 . If it is determined that there is no blurriness, then the noise flag is set as false at  530 . The noise flag, set as either true or false, is then stored at  540 . 
       FIG. 10  illustrates an example of an image of a document containing noise, as well as the decision tree and processing involved in determining whether regions of the document in the image have noise. As seen in  FIG. 10 , the image of the document  1000  contains portions that may be obscured by noise. A decision tree may be implemented to determine if a region of the image comprises too much noise. As seen in  1010 , the decision tree may comprise threshold levels for noise and signal values, with pass and fail regions designated based on whether the signals are above or below one or both of the thresholds. For example, a noise threshold of 6.5 and a signal threshold of 132.5 may be set. A document image with a signal value below the 132.5 threshold could be considered acceptable, regardless of the noise value. Similarly, a document image with a noise value less than 6.5 could be considered acceptable, regardless of the signal value. However, as shown in  1010 , when the document image has both a signal value above the signal threshold and a noise level above the noise threshold, it falls within the “fail” region of the decision tree and the document image is indicated as having unacceptable noise. 
     Again referring back to  FIG. 2 , a blurriness determination  235  may also be performed on the stored gray scale image  320 .  FIG. 6  illustrates the details in the processing step of determining the presence of blurriness  235  in  FIG. 2 . As seen in  FIG. 6 , a Laplacian filter is performed at  605  on the stored gray scale image  320 . Then, the system calculates a certain threshold percentile histogram value of the gray scale image plane at  607 . To calculate a histogram, the values in the image are iterated over all the columns and rows of pixels making up the image. For each value, a count is kept. When all the values have been examined and counted, a histogram is generated. The certain threshold percentile is the histogram value where that percent of total number of elements have been counted. For example, a 90 th  percentile histogram value would be the value at which 90 percent of the pixels would be counted. At steps  609  and  610 , minimum and maximum values of the gray scale image plane are calculated, as detailed above with respect to  FIG. 4 . At  615  and  620 , the mean value and standard deviation values of the gray scale image plane are calculated, using the same techniques as detailed above with respect to  FIG. 4 . At step  625 , a blurriness learning tree is applied, which checks to see if the calculated standard deviation is above or below a predetermined value. The details of this learning tree are shown in  FIG. 11 , which illustrates an example of an image of document containing blurriness  1100 . Applying the learning tree at  1110 , the system checks to see if the calculated standard deviation is above or below the predetermined value. If the standard deviation is above the value, the gray scale image is presumed to be sharp, and a “pass” is indicated by the decision tree. However, if the standard deviation is below the predetermined value, the image is presumed to be blurry, and a “fail” is indicated by the decision tree. Graph  1120  in  FIG. 11  illustrates the outcomes of the decision tree for calculated standard deviations above and below the predetermined value. Referring back to  FIG. 6 , at step  627  the system determines if the gray scale image contains unacceptable blurriness. This determination is based on the “pass” or “fail” outcomes from the decision tree are used to set a blurriness flag as either “true” or “false.” As seen in  FIG. 2 , if it is determined that there is unacceptable blurriness, then a blurriness flag is set to “true” at  635 . If it is determined that there is not unacceptable blurriness, then a blurriness flag is set to “false” at  630 . The blurriness flag, set as either “true” or “false” is then stored at  640 . Again referring back to  FIG. 2 , an uneven illumination determination  230  may also be performed on the stored gray scale image  320 .  FIG. 7  illustrates the details in the processing step of determining the presence of uneven illumination  230  in  FIG. 2 . As seen in  FIG. 7 , at  705  a Gaussian blur filter is applied to the gray scale image to create a blurred image plane. At  707  and  709 , the mean value and standard deviation of the blurred image plane is determined, using the method described above in  FIG. 4 . Then, at  710 , a fraction of the mean-blurred image plane is added to the stored gray scale image plane  320 . To do this, the blurred image is subtracted from the mean, and a certain fraction of the result is taken. This fraction is then added back to the stored gray scale image plane to form a resulting image plane. The fraction may be fixed (for example, 0.75 or 75 percent), or may vary depending on the characteristics of the particular image being analyzed.  FIG. 13  illustrates an example of this process. The resulting image plane from this addition is then stored at  715 . The minimum value, and maximum value, mean, and standard deviation of the resulting image plane are determined at steps  720 ,  725 ,  730 , and  735 , using the process described above with respect to  FIG. 4 . A machine learning tree is applied at  740 . The machine learning tree checks to determine if the maximum value of the resulting image plane  715  is greater than a predetermined value. If so, the captured image is considered to have uneven illumination. An example of this machine learning tree is illustrated at  1210  in  FIG. 12 . 
       FIG. 12  also illustrates an example of an image of a document  1200  having uneven illumination. Based on the outcome of the learning tree  740  from  FIG. 7 , the system checks to see if there is uneven illumination in the image of the document at  745 . If it is determined that uneven illumination is not present, an uneven illumination flag is set to “false” at  750 . However, if it is determined that there is uneven illumination, then the uneven illumination flag is set to “true” at  755 . The set uneven illumination flag is then stored at  760 . 
       FIG. 8  illustrates processing that may be performed following the hotspot determination  220 , noise determination  225 , uneven illumination determination  230 , and blurriness determination  235 . As seen in  FIG. 8 , a signal can be provided to the processing system that conforms to a standard traffic light representation. If the blurriness flag is set to true  800 , the processing system may set an output to FAIL  805  and the digital camera system can display a RED or STOP indicator to the user, suggesting that the user should retake the image before leaving. Since blurriness is considered a failure mode operation for optical character recognition. If the uneven illumination flag is true  810 , the output is set to CAUTION which the digital camera system can display as a YELLOW indicator  815  to the user. The YELLOW indicator provides the user with a signal that they should check the captured image to make sure its quality its sufficient for the desired post-processing, giving the user notice that retaking the image may be necessary. Following on, if the hotspot flag is set to true  820 , the digital camera system can display as a YELLOW indicator  815  to the user. Following on, if the noise flag is set to true  830 , the client application can display as a YELLOW indicator  815  to the user. If none of the flags are true (blurriness, noise, uneven illumination, or hotspot), the client application can display a GREEN indicator  840  to the user to give the user feedback that the image captured is acceptable for post processing. By providing this feedback, the system allows a digital camera or mobile device including a camera to be used as a convenient portable document scanner. The feedback as to whether the image quality is acceptable for post processing, unacceptable for post processing, or potentially unacceptable for post processing may be provided by visual or audio notifications. For example, the display on the digital camera device may show a color coded notification (such as the red, green, and yellow described above, although any colors may be used), icons indicating pass, fail, or caution, or text indicating pass, fail, or caution. The notification may also be provide by an audible tone or alert. For example, different tones or different sequences of tones may be used to indicate pass, fail or caution 
     The methods described herein may be implemented using a computer program product. The computer program product can include one or more non-transitory, tangible, computer readable storage medium, for example; magnetic storage media such as magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as optical disk, optical tape, or machine readable bar code; solid-state electronic storage devices such as random access memory (RAM), or read-only memory (ROM); or any other physical device or media employed to store a computer program having instructions for controlling one or more computers to practice the method according to the present invention.