Abstract:
Methods and systems are provided for defining and determining a formal and verifiable mobile document image quality and usability (MDIQU) standard, or Standard for short. The Standard ensures that a mobile image can be used in an appropriate mobile document processing application, for example an application for mobile check deposit. In order to quantify the usability, the Standard establishes 5 quality and usability grades. A mobile image capture device can capture images. A mobile device can receive information associated with one or more image quality assurance (IQA) criteria; evaluating the images to select an image satisfying an image quality criteria based on the received information; and in response to the image satisfying the image quality score, sending the selected image to determine a set of image quality assurance (IQA) scores.

Description:
RELATED APPLICATIONS 
       [0001]    The present application is a continuation of U.S. patent application Ser. No. 15/397,639, filed on Jan. 3, 2017, which is a continuation of U.S. patent application Ser. No. 14/217,170, filed on Mar. 17, 2014, now issued as U.S. Pat. No. 9,536,139, which claims priority to provisional application No. 61/802,039, filed on Mar. 15, 2013, all which are hereby incorporated by reference in their entirety. 
     
    
     BACKGROUND 
     1. Field of the Invention 
       [0002]    The embodiments described herein relate to processing images captured using a mobile device, and more particularly to developing standards for processing the images and assessing an image processing system to determine whether it meets the standards. 
       2. Related Art 
       [0003]    The use of mobile devices to capture images is ubiquitous in modern culture. Aside from taking pictures of people, places and events, users are utilizing the cameras on their devices for many different purposes. One of those purposes is capturing images of content that the user later wants to review, such as a description of a product, a document, license plate, etc. 
         [0004]    Financial institutions and other businesses have become increasingly interested in electronic processing of checks and other financial documents in order to expedite processing of these documents. Some techniques allow users to scan a copy of the document using a scanner or copier to create an electronic copy of the document that can be digitally processed to extract content. This provides added convenience over the use of a hardcopy original, which would otherwise need to be physically sent or presented to the recipient for processing. For example, some banks can process digital images of checks and extract check information from the image needed to process the check for payment and deposit without requiring that the physical check be routed throughout the bank for processing. 
         [0005]    Mobile devices that incorporate cameras are ubiquitous and may also be useful to capture images of financial documents for mobile processing of financial information. The mobile device may be connected with a financial institution or business through a mobile network connection. However, the process of capturing and uploading images of financial documents using the mobile device is often prone to error, producing images of poor quality which cannot be used to extract data. The user is often unaware of whether the captured document image is sufficient and capable for processing by a business or financial institution. 
         [0006]    Attempts have been made to improve the quality of mobile images of financial documents to improve the accuracy of information extracted therefrom. There are numerous ways in which an image can be improved for extracting its content, some of which are implemented individually and some of which are implemented together. However, it is difficult to determine which methods are the best at improving image quality and content extraction. Of the methods often used, it is even more difficult to select a threshold of that particular method which will provide an accurate capture in as little time as possible. Finally, determining whether an image processing system is capable of performing adequate image capture, processing and content extraction has not been explored. 
         [0007]    Therefore, there is a need for identifying image processing techniques which will provide optimal image correction and accurate content extraction. 
       SUMMARY 
       [0008]    Embodiments described herein provide methods for defining and determining a formal and verifiable mobile document image quality and usability (MDIQU) standard, or Standard for short. The goal of this Standard is to ensure that a mobile image can be used in an appropriate mobile document processing application, for example an application for mobile check deposit, mobile bill pay, mobile balance transfer or mobile insurance submission and application. In order to quantify the usability, the Standard establishes 5 quality and usability grades: the higher grade images will tend to produce higher accuracy results in the related “mobile” application. A mobile image is first tested to determine if the quality is sufficient to obtain content from the image by performing multiple different image quality assessment tests. If the image quality is sufficient, one or more document usability computations are made to determine if the document or content in the image is usable by a particular application. A ranking is then assigned to the image based on the results of the tests which is indicative of the quality and usability of the image. 
         [0009]    Systems and methods are provided for developing standards of image processing for mobile image capture and assessing whether mobile processing engines meet the standards. A mobile processing engine (MDE) is evaluated to determine if it can perform one or more technical capabilities for improving the quality of and extracting content from an image of a financial document (such as a check). A verification process then begins, where the MDE performs the image quality enhancements and text extraction steps on sets of images from a test deck of good and bad images of financial documents with known content. The results of the processing of the test deck are then evaluated by comparing confidence levels with thresholds to determine if each set of images should be accepted or rejected. Further analysis determines whether any of the sets of images were falsely accepted or rejected, and an overall error rate is computed. The overall error rate is then compared with minimum accuracy criteria, and if the criteria are met, the MDE meets the standard for mobile deposit 
         [0010]    The goal of this Standard is to ensure that a mobile image can be used in an appropriate mobile document processing application, primarily Mobile Deposit [1], Mobile Bill Pay [2], Mobile Balance Transfer [3] and Mobile Insurance [4]. 
         [0011]    Other features and advantages should become apparent from the following description of the preferred embodiments, taken in conjunction with the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]    Various embodiments disclosed herein are described in detail with reference to the following figures. The drawings are provided for purposes of illustration only and merely depict typical or exemplary embodiments. These drawings are provided to facilitate the reader&#39;s understanding and shall not be considered limiting of the breadth, scope, or applicability of the embodiments. It should be noted that for clarity and ease of illustration these drawings are not necessarily made to scale. 
           [0013]      FIG. 1  illustrates a method of assessing, grading and ranking an image to determine an image quality and an image usability, according to one embodiment of the invention. 
           [0014]      FIG. 2  illustrates a system for assessing, grading and ranking the image utilizing a mobile image capture device and image server, according to one embodiment of the invention. 
           [0015]      FIG. 3  is an image illustrating an example of an out-of-focus (OOF) image identified during an OOF image quality test, according to one embodiment of the invention. 
           [0016]      FIGS. 4A and 4B  are images of plain skew and view skew images, respectively, identified during a skew image quality test, according to one embodiment of the invention. 
           [0017]      FIGS. 5A and 5B  are images of a low contrast image and poor darkness image, respectively, identified during a low internal contrast image quality test, according to one embodiment of the invention. 
           [0018]      FIG. 6A  is an image of a document where the corners are cut off, as would be identified during a cut corner image quality test, according to one embodiment of the invention. 
           [0019]      FIG. 6B  is an image of a warped document which would be identified during a warpage image quality test, according to one embodiment of the invention. 
           [0020]      FIG. 7  is a block diagram that illustrates an embodiment of a computer/server system upon which an embodiment of the inventive methodology may be implemented 
       
    
    
       [0021]    The various embodiments mentioned above are described in further detail with reference to the aforementioned figured and the following detailed description of exemplary embodiments. 
       DETAILED DESCRIPTION 
       [0022]    I. Summary of Mobile Deposit Standards 
         [0023]    Embodiments described herein define formal and verifiable mobile document image quality and usability (MDIQU) standard, of Standard for short. The goal of this Standard is to ensure that a mobile image can be used in an appropriate mobile document processing application, primarily mobile check deposit, mobile bill pay, mobile balance transfer or mobile insurance submission and application, although this list is only illustrative of the types of images and documents which may take advantage of the methods. 
         [0024]    In order to quantify the usability, the Standard establishes 5 quality and usability grades: the higher grade images will tend to produce higher accuracy results in the related “mobile” application. The standards for various mobile document processing applications may use the ranking system established by this invention in order to define the various accuracy levels. For example, a mobile deposit standard may require MICR accuracy of 99% on mobile images of checks which are Grade 1 (the highest) according to this Standard, 97% on Grade 2 mobile images etc. The lowest grade (or 2 lowest grades, depending on application preferences) defines unusable images for which no meaningful prediction of accuracy could be made. 
         [0025]    Given an application (e.g. Mobile Deposit) and a mobile image, MDIQU answers two basic questions:
       1. Does the mobile image have good image quality?   2. Does the image contain a document which is valid for the particular application it&#39;s intended for (a valid US check in case of MD)?       
 
         [0028]    Moreover, if the image quality is characterized as bad, MDIQU will detect a particular quality defect. 
         [0029]    The answer on the first question (Image Quality) doesn&#39;t depend on the type of document or application in question. All that MDIQU is supposed to verify is that a human would easily read the relevant data off the mobile image. For example, a good quality mobile image of a check must contain the entire check (all 4 corners), be high contrast/sharp with all the characters being legible. The same will apply to mobile images of a bills, driver&#39;s licenses etc. 
         [0030]    The answer on the second question (Document Validity) is supposed to ensure that the document within the image is indeed a check, a bill, a driver&#39;s license etc. Therefore, the validity factor is defined for each application independently. 
         [0031]    II. MDIQU System and Workflow 
         [0032]      FIG. 1  illustrates a flowchart of a workflow for assessing mobile image quality standards, according to one embodiment of the invention. The steps may include: 
         [0033]      100 —accept mobile image under examination and an expected document type 
         [0034]      200 —compute individual IQA scores 
         [0035]      300 —output individual IQA scores 
         [0036]      400 —compute compound IQA scores (see Section 4) 
         [0037]      450 —output compound IQA scores 
         [0038]      600 —make decision if image quality is sufficient to proceed 
         [0039]      700 —if “No”, assign image the lowest rank and exit workflow (in this case, the document can&#39;t be even extracted from the image). 
         [0040]      750 —if “Yes,” receive document category and optionally set of critical fields 
         [0041]      800 —compute usability score from document category and critical fields 
         [0042]      850 —output document usability score 
         [0043]      900 —compares usability score to pre-defined threshold 
         [0044]      1000 —if score is low, assign Rank 3 of Rank 4, then exit. 
         [0045]      1100 —if score is high, assign Rank 1 of Rank 2, then exit. 
         [0046]    One embodiment of an MDIQU system is illustrated in  FIG. 2 , where a mobile image capture device  102  with an image quality assurance database  104  is connected with an image server  106  with a document validity database  108 . Thus, the mobile device  102  may perform one or more of the IQA steps based on information stored in the IQA DB  104  to determine an image quality score, after which the image (if sufficient) is sent to the image server for performing a document validity test using information stored in the document validity DB  108 . An image with a sufficient quality and usability may then be transmitted to an application server  110  where an application will utilize the image and its content for a particular task. Although not illustrated herein, the system may also perform the IQA tests at the image server  106 , and even perform the document usability tests at the mobile device  102 . 
         [0047]    III. Individual Mobile Image Quality Tests 
         [0048]    In one embodiment, the MDIQU system performs the following image quality tests (Mobile IQA tests) to detect the following image deficiencies: Out-of-focus (OOF), Shadow, Small size, View Skew (perspective distortion), Plain Skew, Warped, Low Internal Contrast, Reflection, Cut Corners, Cut Sides, Busy Background and Low External Contrast. Each score should be returned on the scale of 0 to 1000, when 0 means “No defect” and 1000 means “Severe Defect”. Alternately, MDIQU system may detect absence of the defects, thus swapping 0 and 1000. 
         [0049]    U.S. Pat. No. 8,582,862, issued Nov. 12, 2013, gives a description of the Mobile IQA tests and corresponding algorithms, and is herein incorporated by reference in its entirety. Below is a brief description of IQA tests along with their relative importance. 
         [0000]    
       
         
               
               
               
             
           
               
                   
               
               
                 IQA 
                   
                   
               
               
                 Score 
                 Importance 
                 How it gets computed 
               
               
                   
               
             
             
               
                 OOF 
                 High for all 
                 OOF is computed on grey-scale snippets of documents based on 
               
               
                   
                 applications 
                 frequencies of high-contrast local areas. The more high-contrast 
               
               
                   
                   
                 areas is found, the higher score becomes (1000 means 
               
               
                   
                   
                 “absolutely sharp”) 
               
               
                 Shadowed 
                 High for 
                 “Shadowed” score is computed on grayscale snippets of 
               
               
                   
                 MD, 
                 documents. The engine tries to break the entire snippet into 2 
               
               
                   
                 Medium 
                 areas of very different brightness. For each such representation 
               
               
                   
                 otherwise 
                 (could be several), the engine takes into account the delta in 
               
               
                   
                   
                 brightness and the size of the “darker” area. If the delta is high 
               
               
                   
                   
                 enough and the size of “darker” area is close to 50%, a “perfect” 
               
               
                   
                   
                 shadow is registered, making the score = 0. 1000 means “no 
               
               
                   
                   
                 shadow” 
               
               
                 Reflection 
                 High for 
                 Thus IQA score was designed to detect glare on the image and 
               
               
                   
                 Insurance, 
                 assign a numeric value to its size so that 0 would mean “no 
               
               
                   
                 low 
                 glare”, and 1000 would mean “significant” glare 
               
               
                   
                 otherwise 
               
               
                 Low 
                 Medium 
                 Firstly, the engine computes the brightness histogram using 
               
               
                 Internal 
                   
                 grayscale snippet of the document. The histogram is a 256-values 
               
               
                 Contrast 
                   
                 vector H[ ], where H[0] is the number of pixels with brightness 0 
               
               
                   
                   
                 (absolutely black) . . . up to H[255] is the number of pixels with 
               
               
                   
                   
                 brightness 0 (absolutely white). Secondly, having computed the 
               
               
                   
                   
                 histogram H, the engine finds out where ⅓ of all pixels are 
               
               
                   
                   
                 located counting from 0 upwards (G1) and from 255 downwards 
               
               
                   
                   
                 (G2). Then weighted averages of brightness are computed for [0, 
               
               
                   
                   
                 G1] and [G2, 255], which could be considered average 
               
               
                   
                   
                 “darkness” (D) and average “lightness” (L) of the snippet 
               
               
                   
                   
                 respectively. The difference L − D describes the contrast. After 
               
               
                   
                   
                 proper normalization, 0 means very poor contrast and 1000 
               
               
                   
                   
                 means very high contrast 
               
               
                 Darkness 
                 Medium 
                 The goal of this score is to ensure that the image is not too dark.. 
               
               
                   
                   
                 Firstly, the engine computes the brightness histogram using 
               
               
                   
                   
                 grayscale snippet of the document as explained above. Then a 
               
               
                   
                   
                 weighted average grayscale value (G) is computed. After proper 
               
               
                   
                   
                 normalization G = 255 is translated into 1000 (document is pure 
               
               
                   
                   
                 white), G = 0 is translated into 0 (absolutely black) 
               
               
                 Plain 
                 Low 
                 This IQA reflects 2D skew of the document (no skew means the 
               
               
                 Skew 
                   
                 document&#39;s sides are parallel to the image sides). After 
               
               
                   
                   
                 normalization, 1000 means 0 degrees skew while 0 means 45 
               
               
                   
                   
                 degrees (45 degrees is the maximum: further 2D rotation will 
               
               
                   
                   
                 mean that the document changed orientation). The importance of 
               
               
                   
                   
                 detection view skew is low as Moobile SDK handles hight degree 
               
               
                   
                   
                 of 2D skew nicely. 
               
               
                 View 
                 Mid 
                 This IQA reflects 3D (view) angle between the camera and the 
               
               
                 Skew 
                   
                 document No view angle means that the camera view is 
               
               
                   
                   
                 perpendicular to the the document surface. When the view angle 
               
               
                   
                   
                 becomes significant, the document becomes perspectively 
               
               
                   
                   
                 distorted. After normalization, 1000 means 0 degrees skew while 
               
               
                   
                   
                 0 means 22 degrees, 
               
               
                 Cut 
                 High 
                 This IQA reflects presence of all four corners of the document 
               
               
                 Corners 
                   
                 within the mobile image. The engine finds out if a corner is 
               
               
                 (side) 
                   
                 located outside of the image as part of the automatic 
               
               
                   
                   
                 framing/cropping process. Depending on how big is the missing 
               
               
                   
                   
                 (cut-off) part of the corner, the engine computes a penalty per 
               
               
                   
                   
                 corner. Zero penalty means that the corner resides inside the 
               
               
                   
                   
                 image, maximum penalty is assigned when at least 25% (by area) 
               
               
                   
                   
                 of the image quadrant related to this corner is cut-off. Then 
               
               
                   
                   
                 maximum of 4 penalties computed for all for corners becomes the 
               
               
                   
                   
                 overall cut-corner penalty. After normalization, 1000 means that 
               
               
                   
                   
                 no penalty was computed for all corners; 0 means that at least 1 
               
               
                   
                   
                 (unspecified) corner was penalized by max. penalty (see above) 
               
               
                 Small 
                 Low 
                 This score is computed by comparing the document snippet size 
               
               
                 Image 
                   
                 against the mobile image size (the latter becomes known after 
               
               
                   
                   
                 cropping). When the camera moves away from the document, the 
               
               
                   
                   
                 snippet becomes smaller, thus causing low values of this IQA 
               
               
                   
                   
                 metric. After normalization, the score of 1000 means that the 
               
               
                   
                   
                 document occupies at least ⅓ rd  of the entire mobile image area, 
               
               
                   
                   
                 while 0 means that it occupies less than ⅙ th  of the entire mobile 
               
               
                   
                   
                 image area. 
               
               
                   
                   
                 The importance of detection Small Image is low as Moobile SDK 
               
               
                   
                   
                 handles even small images quite nicely. 
               
               
                 Warpage 
                 None for 
                 The score is computed as a measure of how flat the document is. 
               
               
                   
                 DLs, 
                 The way to compute that is to measure how far the snippet sides 
               
               
                   
                 Medium 
                 deviate from ideal straight lines. To normalize such deviation, its 
               
               
                   
                 otherwise 
                 maximum value along side is divided by the perpendicular side&#39;s 
               
               
                   
                   
                 length. For example, to compute “flatness” along the upper 
               
               
                   
                   
                 horizontal side of the document, the largest deviation (in pixels) 
               
               
                   
                   
                 is divided by the document snippet height. In the end, the largest 
               
               
                   
                   
                 normalized deviation is taken, and then further normalized to 
               
               
                   
                   
                 become IQA: if the document is absolutely flat, the score 
               
               
                   
                   
                 becomes 1000; the deviation of 25% and more becomes 0. 
               
               
                 Low 
                 High for 
                 There is insufficient contrast between the document and the 
               
               
                 External 
                 DLs, 
                 background (making it harder to located the document) 
               
               
                 Contrast 
                 otherwise 
               
               
                   
                 medium 
               
               
                 Busy 
                 High for 
                 Too many contrasting objects detected in the background 
               
               
                 Background 
                 DLs, 
                 (making it harder to located the document) 
               
               
                   
                 otherwise 
               
               
                   
                 medium 
               
               
                   
               
             
          
         
       
     
       Examples of Individual Mobile Defects 
       [0050]      FIG. 3  illustrates an image which is out of focus (OOF) and would be identified with an OOF IQA test. The algorithm may be modified to be computed only “on characters”. This means that a special filter is used to detect positions of text characters, which then are used in the computation while the other areas are ignored.  FIG. 3  illustrates an example of poor OOF (OOF score=90). 
         [0051]    Reflection IQA testing doesn&#39;t work well on the image level and needs to be “localized” to the field level. 
         [0052]      FIG. 4A  illustrates an image of a document with poor plain skew (IQA=200), while  FIG. 4B  illustrates an image of a document with a view skew. 
         [0053]      FIG. 5A  illustrates an image with low contrast (specifically a low contrast IQA=210). This IQA should not be confused with Low Contrast background: the former describes contrasts inside the document whereas the latter outside (between the document and background).  FIG. 5B  illustrates an image which suffers from darkness, and therefore has a darkness IQA score of 290. 
         [0054]      FIG. 6A  illustrates an image of a document with cut corners, receiving a score of 900.  FIG. 6B  illustrates an image of a document with a warped image, receiving a warpage score of 0. 
         [0055]    These IQA scores may be averaged or combined to determine a total score or weighted depending on the relevance of a particular IQA test. Nevertheless, a simple threshold value may be set to determine whether the image has sufficient quality to proceed to the document usability tests. 
         [0056]    IV. Compound IQA Scores 
         [0057]    Embodiments of the invention use individual mobile IQA scores to automatically create two compound IQA scores which will ensure that the mobile document could be detected in the mobile image as well as that the document is fully legible. The first compound IQAs will be titled “Crop IQA” and “Quality IQA.” 
       Benefits of Compound IQAs Over “Individual” IQAs 
       [0058]    Simplicity: There are more than a dozen of individual IQAs and in essence they are being replaced by 2 compounded ones: “crop” and “quality”. It&#39;s much better to move two dials than a dozen. 
         [0059]    Eliminating wrong messages: In reality, some individual IQAs are strongly correlated between themselves and with other defects. For example, shadow and darkness (2 different IQAs) may cause low OOF (another IQA) as also can small image (yet another IQA); wrong crop (has its own IQA) can cause wrong identification and therefore present the image as “unsupported” and so on. By compounding related IQAs we avoid potential misclassifications and get rid of this hierarchy and thresholds altogether. MIP will have a much smaller set of thresholds (3) and the error classifier will be well-tested and optimized in R&amp;D. 
       Description of Compound IQA Scores 
       [0060]    The compound IQA scores are linear combinations of individual IQA score. The coefficients in combination depend on application as different individual IQA score have different importance for different document categories, see table  2 . 
         [0061]    V. Document Usability 
         [0062]    Embodiments of the MDIQU system provide document usability testing to ensure that the document within the image is indeed a check, a bill, a driver&#39;s license etc. 
         [0063]    The following categories may be supported: US Checks (Mobile Deposit), Remittance Coupon (Mobile Bill Pay), Credit Card Bills (Mobile Balance Transfer), and Driver&#39;s License (Mobile Insurance). 
       Definition of Critical Fields 
       [0064]    The usability testing is based on a definition of Critical (or Required) Fields, see  FIG. 1 . If the fields are not specified, the following ones are used by default. 
         [0000]    
       
         
               
               
               
               
               
               
               
               
               
             
           
               
                   
               
               
                   
                 Document 
                   
                   
                   
                   
                   
                   
                   
               
               
                 Application 
                 Category 
                 Field1 
                 Field2 
                 Field3 
                 Field4 
                 Field 5 
                 Field 6 
                 Field 7 
               
               
                   
               
             
             
               
                 Mobile 
                 Checks 
                 MICR 
                 Legal 
                 Courtesy 
                 Endorsement 
                   
                   
                   
               
               
                 Deposit 
                   
                   
                 Amount 
                 Amount 
               
               
                 Mobile 
                 Remittance 
                 Account 
                 Payee.ZIP 
                 Biller 
               
               
                 Bill Pay 
                 Coupons 
                 Number 
                   
                 Name 
               
               
                 Balance 
                 Credit 
                 Account 
                 Payee.ZIP 
                 Biller 
                 Balance 
               
               
                 Transfer 
                 Card 
                 Number 
                   
                 Name 
                 Due 
               
               
                   
                 Bills 
               
               
                 Mobile 
                 Driver 
                 DL 
                 Name 
                 Address 
                 DoB 
                 Class 
                 Sex 
                 Expiration 
               
               
                 Insurance 
                 License&#39;s 
                 Number 
                   
                   
                   
                   
                   
                 Date 
               
               
                   
               
             
          
         
       
     
       Computation of Usability Score 
       [0065]    The usability computation involves data capture from the document image cropped out of the mobile image. Present MDIQU system uses a Mobile Preprocessing Engine and Dynamic Data Capture engine described in U.S. Pat. No. 8,379,914, which is incorporated herein by reference in its entirety. 
         [0066]    The Document usability score is computed based on the set of captured critical fields or/and the confidence of each of such fields. As with the image quality test, the document usability tests results in a yes or no answer to the question of whether the document is usable, and will then further analyze the response to assign a grade value to the document based on how usable (or un-usable) it is. 
       Computer-Implemented Embodiment 
       [0067]      FIG. 7  is a block diagram that illustrates an embodiment of a computer/server system  700  upon which an embodiment of the inventive methodology may be implemented. The system  700  includes a computer/server platform  701  including a processor  702  and memory  703  which operate to execute instructions, as known to one of skill in the art. The term “computer-readable storage medium” as used herein refers to any tangible medium, such as a disk or semiconductor memory, that participates in providing instructions to processor  702  for execution. Additionally, the computer platform  701  receives input from a plurality of input devices  704 , such as a keyboard, mouse, touch device or verbal command. The computer platform  701  may additionally be connected to a removable storage device  705 , such as a portable hard drive, optical media (CD or DVD), disk media or any other tangible medium from which a computer can read executable code. The computer platform may further be connected to network resources  706  which connect to the Internet or other components of a local public or private network. The network resources  706  may provide instructions and data to the computer platform from a remote location on a network  707 . The connections to the network resources  706  may be via wireless protocols, such as the 802.11 standards, Bluetooth® or cellular protocols, or via physical transmission media, such as cables or fiber optics. The network resources may include storage devices for storing data and executable instructions at a location separate from the computer platform  701 . The computer interacts with a display  708  to output data and other information to a user, as well as to request additional instructions and input from the user. The display  708  may therefore further act as an input device  704  for interacting with a user. 
         [0068]    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 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 described embodiments are 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 of the described embodiments. 
         [0069]    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.