# Patent

## Title
Systems and methods for assessing standards for mobile image quality

## Abstract

Methods 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 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.

## Background
RELATED APPLICATIONS
The present application is a continuation of U.S. patent application Ser. No. 14/217,170, filed on Mar. 17, 2014, which is 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
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
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.
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.
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.
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.
Therefore, there is a need for identifying image processing techniques which will provide optimal image correction and accurate content extraction.
SUMMARY
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.
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
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].
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
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'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.
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.
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.
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.
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.
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.
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.
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.
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



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
I. Summary of Mobile Deposit Standards
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.
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.
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's intended for (a valid US check in case of MD)?
    
    


Moreover, if the image quality is characterized as bad, MDIQU will detect a particular quality defect.
The answer on the first question (Image Quality) doesn'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's licenses etc.
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's license etc. Therefore, the validity factor is defined for each application independently.
II. MDIQU System and Workflow
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:
100—accept mobile image under examination and an expected document type
200—compute individual IQA scores
300—output individual IQA scores
400—compute compound IQA scores (see Section 4)
450—output compound IQA scores
600—make decision if image quality is sufficient to proceed
700—if “No”, assign image the lowest rank and exit workflow (in this case, the document can't be even extracted from the image).
750—if “Yes,” receive document category and optionally set of critical fields
800—compute usability score from document category and critical fields
850—output document usability score
900—compares usability score to pre-defined threshold
1000—if score is low, assign Rank 3 of Rank 4, then exit.
1100—if score is high, assign Rank 1 of Rank 2, then exit.
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.
III. Individual Mobile Image Quality Tests
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.
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.












IQA




Score
Importance
How it gets computed







OOF
High for all
OOF is computed on grey-scale snippets of documents based



applications
on 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



otherwise
representation (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-


Contrast

values 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 1⁄3 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


Skew

the document'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


Image

size 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 1⁄3rd of the




entire mobile image area, while 0 means that it occupies less




than 1⁄6th 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



DLs,
is. The way to compute that is to measure how far the snippet



Medium
sides deviate from ideal straight lines. To normalize such



otherwise
deviation, its maximum value along side is divided by the




perpendicular side'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
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).
Reflection IQA testing doesn't work well on the image level and needs to be “localized” to the field level.
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.
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.
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.
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.
IV. Compound IQA Scores
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
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's much better to move two dials than a dozen.
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&D.
Description of Compound IQA Scores
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.
V. Document Usability
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's license etc.
The following categories may be supported: US Checks (Mobile Deposit), Remittance Coupon (Mobile Bill Pay), Credit Card Bills (Mobile Balance Transfer), and Driver's License (Mobile Insurance).
Definition of Critical Fields
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.



















Document









Application
Category
Field 1
Field 2
Field 3
Field 4
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 Bills
Number

Name
Due


Mobile
Driver
DL
Name
Address
DoB
Class
Sex
Expiration


Insurance
License's
Number





Date









Computation of Usability Score

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.
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
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.
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.
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.

## Claims

1. A non-transitory computer-readable medium comprising instructions which, when executed by a processor, cause the processor to:
receive a set of images associated with a document;
evaluate the set of images to select an image of the set of images satisfying an image quality criteria;
in response to the image satisfying the image quality criteria, process the selected image to determine a set of image quality assurance (IQA) scores, comprising:
performing a computation relating to a set of image quality assurance (IQA) scores for the selected image to obtain at least one compound IQA score, and wherein the set of IQA scores is based on at least combining a subset of strongly correlated IQA scores in the set of IQA scores into a compound IQA score, and wherein the subset of strongly correlated IQA scores is associated with a common image defect;
in response to at least one compound IQA score associated with the selected image of the set of images satisfying a predetermined standard of image quality:
select the set of images as accepted.

2. The non-transitory computer-readable medium of claim 1, wherein to satisfy an image quality criteria, content is at least extractable from the selected image.

3. The non-transitory computer-readable medium of claim 1, wherein in response to an IQA score associated with the selected image of the set of images not satisfying the predetermined standard of image quality the processor is further to evaluate the set of images to select another image of the set of images satisfying the image quality criteria.

4. The non-transitory computer-readable medium of claim 1, wherein the processor is further to determine a false selection in response to an IQA score associated with the selected image of the set of images not satisfying the predetermined standard of image quality.

5. The non-transitory computer-readable medium of claim 4, wherein the processor is further to calculate a false selection rate of the set of images.

6. The non-transitory computer-readable medium of claim 1, wherein in response to the set of images failing to satisfy the image quality criteria; rejecting the set of images.

7. The non-transitory computer-readable medium of claim 1, wherein the set of IQA scores includes one or more of: an out-of-focus (OOF) score, a shadow score, a small-image-size score, a view-skew score, a plain-skew score, a warpage score, a low-internal-contrast score, a reflection score, a cut-corners score, a cut-sides score, a busy-background score, and a low-external-contrast score.

8. The non-transitory computer-readable medium of claim 1, wherein performing the computation relating to the set of IQA scores to obtain at least one compound IQA score includes computing a linear combination of the set of IQA scores.

9. The non-transitory computer-readable medium of claim 1, wherein to satisfy the predetermined standard of image quality, the processor is further to:
assess a usability score of the document associated with the set of images to verify that the document belongs to one of a set of supported categories of documents.

10. The non-transitory computer-readable medium of claim 9, wherein assessing the usability score of the document includes:
capturing a set of critical fields from the image of the document; and
determining the usability score based on the set of captured critical fields.

11. A system for assessing mobile image qualities of a document, comprising:
a processor; and
a memory coupled to the processor to store instructions for assessing qualities of mobile images of documents;
wherein the processor is configured to:
receive a set of images associated with a document;

evaluate the set of images to select an image of the set of images satisfying an image quality criteria;
in response to the image satisfying the image quality criteria, process the selected image to determine a set of image quality assurance (IQA) scores, comprising:
performing a computation relating to a set of image quality assurance (IQA) scores for the selected image to obtain at least one compound IQA score, and wherein the set of IQA scores is based on at least combining a subset of strongly correlated IQA scores in the set of IQA scores into a compound IQA score, and wherein the subset of strongly correlated IQA scores is associated with a common image defect;
in response to at least one compound IQA score associated with the selected image of the set of images satisfying a predetermined standard of image quality:
select the set of images as accepted.

12. The system of claim 11, wherein to satisfy an image quality criteria, content is at least extractable from the selected image.

13. The system of claim 11, wherein in response to an IQA score associated with the selected image of the set of images not satisfying the predetermined standard of image quality the processor is further to evaluate the set of images to select another image of the set of images satisfying the image quality criteria.

14. The system of claim 11, wherein the processor is further to determine a false selection in response to an IQA score associated with the selected image of the set of images not satisfying the predetermined standard of image quality.

15. The system of claim 14, wherein the processor is further to calculate a false selection rate of the set of images.

16. The system of claim 11, wherein in response to the set of images failing to satisfy the image quality criteria; rejecting the set of images.

17. The system of claim 11, wherein the set of IQA scores includes one or more of: an out-of-focus (OOF) score, a shadow score, a small-image-size score, a view-skew score, a plain-skew score, a warpage score, a low-internal-contrast score, a reflection score, a cut-corners score, a cut-sides score, a busy-background score, and a low-external-contrast score.

18. The system of claim 11, wherein performing the computation relating to the set of IQA scores to obtain at least one compound IQA score includes computing a linear combination of the set of IQA scores.

19. The system of claim 11, wherein to satisfy the predetermined standard of image quality, the processor is further to:
assess a usability score of the document associated with the set of images to verify that the document belongs to one of a set of supported categories of documents.

20. The system of claim 19, wherein assessing the usability score of the document includes:
capturing a set of critical fields from the image of the document; and
determining the usability score based on the set of captured critical fields.