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`PROVISIONAL APPLICATION FOR PATENT COVER SHEET - Page 1 of 2
`This is a request for filing a PROVISIONAL APPLICATION FOR PATENT under 37 CFR 1.53(c).
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`Given Name (first and middle [if any])
`Grigori
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`Family Name or Surname
`Nepomniachtchi
`
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`San Diego, California
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`Residence
`
`separately numbered sheets attached hereto
`[3 Additional inventors are being named on
`TITLE OF THE INVENTION (500 characters max)
`METHODS AND SYSTEMS FOR MOBILE DEPOSIT IMAGE PROCESSING
`
`Direct all correspondence to:
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`P19SMALL/REV12
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`Doc Code:
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`PROVISIONAL APPLICATION COVER SHEET
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`Page 2 of 2
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`The invention was made by an agency of the United States Government or under a contract with an agency of the United States
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`B Yes, the name of the US. Government agency and the Government contract number are:
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`WARNING:
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`application) or issuance of a patent. Furthermore, the record from an abandoned application may also be available to the
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`card authorization forms PTO-2038 submitted for payment purposes are not retained in the application file and therefore are
`not publicly available.
`1
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`SIGNATURE
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`N
`
`X’
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`9‘
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`i
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`E
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`Date
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`January 13, zoos
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`REGISTRATION NO.
`(if appropriate)
`
`42,651
`
`TYPED or PRINTED NAME
`
`David E. Heisey
`
`TELEPHONE
`
`858-720-8900
`
`Docket Number:
`
`11JN-134958
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`PROVISIONAL
`
`APPLICATION
`
`of
`
`Giigori Nepomniachtchi
`
`For
`
`UNITED STATES LETTERS PATENT
`
`On
`
`METHODS AND SYSTEMS FOR MOBILE DEPOSIT IMAGE PROCESSING
`
`Attorney Docket No.: 11JN~134958
`
`Sheets of Drawings: Six (6)
`
`Attorneys
`
`SHEPPARD MULLIN RICHTER & HAMPTON LLP
`
`333 South Hope Street
`Forty-Eighth Floor
`Los Angeles, CA 90071
`Telephone:
`(858)720-8900
`Facsimile:
`(858) 509-3691
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`Field of the Invention
`
`[0001]
`
`The present invention relates generally to automated document processing and
`
`more particularly, to methods and systems for document image processing using mobile devices.
`
`Background of the Invention
`
`[0002]
`
`In general, financial institutions have automated most check processing systems
`
`by printing financial documents, such as account numbers and bank routing numbers, onto the
`
`checks. Before a check amount is deducted from a payer's account, the amount, account number,
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`and other important information must be extracted from the check. This highly automated form
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`of extraction is done by a check processing control system that captures information from the
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`Magnetic Ink Character Recognition ("MICR") line. The MICR line consists of specially
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`designed numerals that are printed on the bottom of a check using magnetic ink. The MICR data
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`fields include the bank routing number, bank transit number, account number, check serial
`
`number, check amount, process code and extended process code.
`
`[0003]
`
`Checks and other financial documents may be processed by banks and other
`
`financial institutions in large numbers. The documents that may be processed might include
`
`checks, deposit slips, payment slips, etc. In some cases the banks or other financial institutions
`
`may be required to use the actual physical documents. For example, checks might need to be
`
`transported between multiple banks or other financial institutions. This may slow down the
`
`processing of financial documents.
`
`Summary of the Invention
`
`[0004]
`
`In order to facilitate financial document processing for people receiving checks,
`
`payment slips, etc. some embodiments of the systems and methods described herein may allow
`
`the users to transmit images of the financial documents using a mobile communication device.
`
`Transmitting an image of a financial document might generally be quicker and easier than
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`transmitting the actual document. Additionally, if methods and systems can be found that allow
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`the transmission of such information using a mobile communication device such as, for example,
`
`a mobile telephone handset, then large numbers of people may benefit from these systems and
`
`methods because a large number of people currently carry and use handheld mobile
`
`communication derives.
`
`[0005]
`
`The present invention relates generally to automated document processing and
`
`more particularly, to methods and systems for document image processing using mobile devices.
`
`In accordance with various embodiments, methods and systems for document capture on a
`
`mobile communication device are provided. These methods and systems may comprise
`
`capturing an image of a document using a mobile communication device; submitting the image
`
`to a server; and processing the image to create a bi—tonal image of the document for data
`
`extraction.
`
`[0006]
`
`In accordance with various embodiments, methods and systems for document
`
`capture on a mobile communication device may further comprise requiring a user to login into an
`
`application. In this way access to the document capture system using a mobile communication
`
`device might be limited to authorized users. The methods and systems may further comprise
`
`selecting a type of document and entering an amount. Some systems may receive a status at the
`
`mobile communication device.
`
`[0007]
`
`In accordance with various embodiments, processing the image may comprise
`
`processing the image on the mobile communication device, processing the image on the server or
`
`processing the image on the mobile communication device and the server. Processing the image
`
`may comprise converting the image to grayscale, detecting a quadrangle and correcting the
`
`image. In some embodiments, processing the image may comprise converting the image to a bi-
`
`tonal image.
`
`[0008]
`
`Other features and advantages of the present invention should become apparent
`
`from the following description of the preferred embodiments, taken in conjunction with the
`
`accompanying drawings, which illustrate, by way of example, the principles of the invention.
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`Brief Description of the Drawings
`
`[0009]
`
`'
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`Embodiments of the present invention will now be described, by way of example
`
`only, with reference to the following drawings, in which:
`
`[0010]
`
`Figure 1 is a diagram illustrating an example check that might be imaged with the
`
`systems and methods described herein;
`
`[0011]
`
`Figure 2 is a diagram illustrating an example payment coupon that might be
`
`imaged using the systems and methods described herein;
`
`[0012]
`
`Figure 3 is a diagram illustrating an example out~of—focus image of the check
`
`illustrated in Figure 1;
`
`[0013]
`
`Figure 4 is a diagram illustrating an example out—of~focus image of the payment
`
`coupon illustrated in Figure 2;
`
`[0014]
`
`Figure 5 is a diagram illustrating an example of perspective distortion in an image
`
`of a rectangular shaped document;
`
`[0015]
`
`Figure 6 is a diagram illustrating an example original image, focus rectangle and
`
`document quadrangle ABCD in accordance with the example of Figure 5;
`
`[0016]
`
`Figure 7 is a flowchart illustrating an example method in accordance with the
`
`systems and methods described herein;
`
`[0017]
`
`Figure 8 is a flowchart illustrating an example method in accordance with the
`
`systems and methods described herein;
`
`[0018]
`
`Figure 9 is a diagram illustrating an example bi—tonal image of the check of
`
`Figures 1 and 3 in accordance with the systems and methods described herein; and
`
`[0019]
`
`Figure 10 is a diagram illustrating an example bi—tonal image of the payment
`
`coupon of Figures 2 and 4 in accordance with the systems and methods described herein.
`
`Detailed Description of the Preferred Embodiments
`
`[0020]
`
`Figure 1 is a diagram illustrating an example check 100 that might be imaged with
`
`the systems and methods described herein. The mobile deposit image processing systems and
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`methods may be used with personal checks, business checks, cashier's checks, certified checks,
`
`warrants or other financial documents. By using an image of the check 100 the check clearing
`
`process might be performed more efficiently. As would be appreciated by those of skill in the
`
`art, checks are not the only type of financial documents that may be used with these systems.
`
`For example, other financial documents, such as deposit slips, might also be processed using the
`
`systems and methods described herein. Figure 2 is a diagram illustrating an example payment
`
`coupon 200 that might be imaged using the systems and methods described herein.
`
`[0021]
`
`In some embodiments, checks 100, payment coupons 200, or other financial
`
`documents might be imaged using a mobile device. The mobile device may be a mobile
`
`telephone handset, Personal Digital Assistant, or other mobile communication device. The
`
`mobile device might include a camera, or might include functionality that allows it to connect to
`
`a camera. This connection might be wired or wireless. In this way the mobile device may
`
`connect to an external camera and receive images from the camera.
`
`[0022]
`
`Images of the financial documents taken using the mobile device or downloaded
`
`to the mobile device may be transmitted to a server. For example, in some cases, the images
`
`may be transmitted over a mobile communication device network, such as a code division
`
`multiple access ("CDMA") telephone network, or other mobile telephone network. Images taken
`
`using, for example, a mobile device's camera, may be 24 bit per pixel JPG images. It will be
`
`understood, however, that many other types of images might also be taken using different
`
`cameras, mobile devices, etc.
`
`[0023]
`
`Various financial documents may include various fields. Some of the fields in the
`
`financial documents might be considered "primary" fields. For example, the primary fields of
`interest on a check 100 might include the legal 102 and courtesy l04 amounts and the MICR line
`
`106. Other fields of interest may include the payee 108, date 110 and the signature 112. The
`
`primary fields of interest for the payment coupon 200 might include the payment amounts 202,
`
`such as the balance, minimum payment and interest. The billing company name and address
`
`204, the account number 206 and the code—line 208 may also be fields of interest. In some
`
`embodiments it may be necessary to electronically read various information from these fields on
`
`a financial document. For example, in order to process a check that is to be deposited, it might
`
`be necessary to electronically read the legal 102 and courtesy 104 amounts, the MICR line 106,
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`the payee 108, date 110 and the signature 112 on the check. In some cases, this information is
`
`difficult to read because, for example, the check or other financial document is out of focus or is
`
`otherwise poorly imaged.
`
`[0024]
`
`Figure 3 is a diagram illustrating an example out-of—focus image of the check
`
`illustrated in Figure 1. When taking pictures, in some cases, the pictures might be out of focus.
`
`An image of a financial document that is out of focus might be difficult or impossible to read,
`
`electronically process, etc. For example, it might be difficult to read the amount 302 and 304 or
`
`the payee 306 on the image 300 of the check 100. Figure 4 is a diagram illustrating an example
`
`out—of-focus image of the payment coupon illustrated in Figure 2. Because the image 400 of the
`
`payment coupon 200 is out of focus it might be difficult to properly credit the payment. For
`
`example, the payment might be credited to the wrong account or an incorrect amount might be
`
`credited. This may be especially true if a check and a payment coupon are both difficult to read
`
`or the scan quality is poor.
`
`[0025]
`
`Many different factors may affect the quality of an image and the ability of a
`
`mobile device based financial document processing system. Optical defects, such as out-of-
`
`focus images (as discussed above) unequal contrast or brightness or other optical defects might
`
`make it difficult to process an image of a document (e.g., a check, payment coupon, deposit slip,
`
`etc.) The quality of an image may also be affected by the document position on a surface when
`
`photographed, for example, right side up, upside down, skewed, etc. Further, if a document is
`
`imaged while upside~down it might be impossible or nearly impossible to for the system to
`
`determine the information contained on the document. Not only will the letters be much lighter
`
`or non-existent in the scan because they are on the back of the page, but generally the letters
`
`might also be backwards, if they show up at all on the image.
`
`[0026]
`
`In some cases, the type of surface might affect the final image. For example, if a
`
`document is sitting on a rough surface when an image is taken, that rough surface might show
`
`through. In some cases the surface of the document might be rough because of the surface below
`
`it. Additionally, the rough surface may cause shadows or other problems that might be picked up
`
`by the camera. These problems might make it difficult or impossible to determine the
`
`information contained on the document.
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`[0027]
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`Lighting may also affect the quality of an image, for example, the location of a
`
`light source and light source distortions. Using a light source above a document might light the
`
`document in a way that improves the image quality, while a light source to the side of the
`
`document might produce an image that is more difficult to process. Lighting from the side
`
`might, for example, cause shadows or other lighting distortions. The type of light might also be
`
`a factor, for example, sun, electric bulb, florescent lighting, etc. If the lighting is too bright, the
`
`document might be washed out in the image. On the other hand, if the lighting is too dark, it
`
`might be difficult to read the image.
`
`[0028]
`
`The quality of the image might also be affected by document features, such as, the
`
`type of document, the fonts used, the colors selected, etc. For example, an image of a white
`document with black lettering might be easier to process than a dark colored document with
`
`black letters. Image quality may also be affected by the mobile device used. Some mobile
`
`camera phones, for example, might have cameras that save an image using a greater number of
`
`mega pixels. Other mobile cameras phones might have an auto~focus feature, automatic flash,
`
`etc. Generally, these features might improve an image when compared to mobile devices that do
`
`not include such features.
`
`[0029]
`
`A document image taken using a mobile device might have one or more of the
`
`defects discussed above. These defects or others might cause low accuracy when processing the
`
`image, for example, when processing one or more of the fields on a document. Accordingly, in
`
`some embodiments, a system using a mobile device to create images of documents may include
`
`the ability to identify poor quality images. If the quality of an image can be detennined a user
`
`may be prompted to take another image.
`
`[0030]
`
`A variety of metrics might be used to detect an out—of-focus image. For example,
`
`a focus measure may be employed. The focus measure may be the ratio of the maximum video
`
`gradient between adjacent pixels measured over the entire image and normalized with respect to
`
`an image's gray level dynamic range and "pixel pitch". The pixel pitch may be the distance
`
`between dots on the image. In some embodiments a focus score might be used to determine if an
`
`image is adequately focused. If an image is not adequately focused, a user might be prompted to
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`take another image.
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`[0031]
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`An image focus score might be calculated as a function maximum video gradient,
`
`gray level dynamic range and pixel pitch. For example, in one embodiment:
`
`Image Focus Score = (Maximum Video Gradient)*(Gray Level Dynamic Range)*(Pixel Pitch)
`
`(eq- 1)
`
`[0032]
`
`The video gradient may be the absolute value of the gray level for a first pixel "i"
`
`minus the gray level for a second pixel "i+l ". For example:
`
`Video Gradient = ABS[(Grey level for pixel "i") — (Gray level for pixel "i+l ")] (eq. 2)
`
`[0033]
`
`The gray level dynamic range may be the average of the "n" lightest pixels minus
`
`the average of the "n" darkest pixels. For example:
`
`Gray Level Dynamic Range = [AVE("N" lightest pixels) — AVE("N" darkest pixels)] (eq. 3)
`
`[0034]
`
`In equation 3 above, N may be defined as the number of pixels used to determine
`
`the average darkest and lightest pixel gray levels in the image. In some embodiments, N might
`
`be chosen to be 64. Accordingly, in some embodiments, the 64 darkest pixels are averaged
`
`together and the 64 lightest pixels are averaged together to compute the gray level dynamic range
`
`value.
`
`[0035]
`per inch.
`
`The pixel pitch may be the reciprocal of the image resolution, for example, in dots
`4
`
`Pixel Pitch = [l / Image Resolution] (eq. 4)
`
`[0036]
`
`In other Words, as defined above, the pixel pitch is the distance between dots on
`
`the image because the Image Resolution is the reciprocal of the distance between dots on an
`
`image.
`
`[0037]
`
`Figure 5 is a diagram illustrating an example of perspective distortion in an image
`
`of a rectangular shaped document. An image may contain perspective transformation distortions
`
`500 such that a rectangle might become a quadrangle ABCD 502, as illustrated in the figure.
`
`The perspective distortion may occur because an image is taken using a camera that is placed at
`
`an angle to a document rather than directly above the document. When directly above a
`
`rectangular document it will generally appear to be rectangular. As the imaging device moves
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`from directly above angularly downward towards the surface, the document distorts until the
`
`document can no longer be seen and only the edge of the page may be seen.
`
`[0038]
`
`The dotted frame 504 comprises the image frame obtained by the camera. The
`
`image frame is be sized h x w, as illustrated in the figure. Generally, it may be preferable to
`
`contain an entire document within the h x w frame of a single image. It will be understood,
`
`however, that some documents might be too large or include too many pages for this to be
`
`preferable or even feasible.
`
`[0039]
`
`In some embodiments, an image might be processed, or preprocessed, to
`
`automatically find and "lift" the quadrangle 502. In other words, the document that forms
`
`quadrangle 502 might be separated from the rest of the image so that the document alone might
`
`be processed. By separating quadrangle 502 from any background in an image, it may then be
`
`further processed.
`
`[0040]
`
`The quadrangle 502 might be mapped onto a rectangular bitmap to hopefully
`
`remove or decrease the perspective distortion. Additionally, image sharpening might be used to
`
`improve the out—of—focus score of the image. The resolution of the image may then be increased
`
`and the image converted to a black-and-white image. In some cases, a black—and—white image
`
`might have a higher recognition rate when processed using an automated document processing
`
`system in ‘accordance with the systems and methods described herein.
`
`[0041]
`
`An image that is bi-tonal, e.g., black-and—white, might be used in some systems.
`
`Such systems might require an image that is at least 200 dots per inch resolution. Accordingly, a
`
`color image taken using a mobile device might need to be high enough quality so that the image
`
`may successfully be converted from, for example, a 24 bit per pixel RGB image to a bi-tonal
`
`image. The image may be sized as if the document, e.g., check, payment coupon, etc., was
`
`scanned at 200 dots per inch.
`
`[0042]
`
`Figure 6 is a diagram illustrating an example original image, focus rectangle and
`
`document quadrangle ABCD in accordance with the example of Figure 5. In some embodiments
`
`it may be necessary to place a document for processing at or near the center of an input image
`
`close to the camera. All points A, B, C and D are located in the image, and the focus rectangle
`
`602 is located inside quadrangle ABCD 502. The document might also have a low 0ut—of—focus
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`score and the background surrounding the document might be selected to be darker than the
`
`document. In this way, the lighter document will stand out from the darker background.
`
`[0043]
`
`Figure 7 is a flowchart illustrating an example method in accordance with the
`
`systems and methods described herein. Referring now to Figure 7, in step 700 a user might log
`
`into a document capture system on a mobile communication device. In accordance with various
`
`embodiments, methods and systems for document capture on a mobile communication device
`
`may further comprise requiring a user to log into an application. In this way, access to the
`
`document capture system using a mobile communication device might be limited to authorized
`
`l.1S€I‘S .
`
`[0044]
`
`In step 702, in the illustrated embodiment, the type of document is selected. For
`
`example, a user might select a document type for a check, payment coupon or deposit slip. By
`
`entering the type of document, a mobile device might be able to scan specific parts of an image
`
`to determine, for example, payee, check amount, signature, etc. In some embodiments, however,
`
`a device might determine what type of document is being imaged by processing the image.
`
`[0045]
`
`In step 704, an image is captured using, for example, a mobile communication
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`device. In the illustrated embodiment an application running on the mobile communication
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`device may prompt the user of the device to take a picture of the front of the document. The
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`back of the document might also be imaged. For example, if the document is a check, an image
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`of the back of the document might be necessary because the back of the check might need to be
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`endorsed. If the back of the document needs to be imaged, the application may prompt the user
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`to take the image. The application might also conduct some image processing to determine if the
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`quality of the image or images is sufficient for further processing in accordance with the systems
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`and methods described herein. The quality needed for further processing might vary from
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`implementation to implementation. For example, some systems might be better able to
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`determine information contained on a poor quality image then other systems.
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`[0046]
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`In the illustrated embodiment, at step 706, an amount is entered. When the
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`document being processed is a check, the amount entered may be the amount of the check.
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`Alternatively, the amount might be an amount of a payment or an amount of a deposit,
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`depending on the type of document being processed.
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`[0047]
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`In some embodiments, the system might determine the amount by processing the
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`image. For example, in some cases, optical character recognition ("OCR") might be used to
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`determine What numbers are present on the financial document. For example, numbers located
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`in the amount box of a check or payment coupon might then be determined using OCR or other
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`computer based character determination. This might be done instead of requiring the amount to
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`be entered manually. In other embodiments, a manual entry might be used to verify a computer
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`generated value that is determined using, for example, OCR or other computer based character
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`determination.
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`[0048]
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`In step 708, the image is transmitted to a server. The image might be transmitted
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`using, for example, hypertext transfer protocol ("HTTP") or mobile messaging service ("MMS").
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`The server might then confirm that the image was received by, for example, transmitting a
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`message back to the mobile device.
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`[0049]
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`In step 710, image processing is performed. In the example embodiment, the
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`server may clean up the image be performing auto-rotate, de—sl<ew, perspective distortion
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`correction, cropping, etc. The server might also process the image to produce a bi-tonal image
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`for data extraction.
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`[0050]
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`In other embodiments, some or all data processing might be performed at the
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`mobile communication device. For example, the mobile communication device might perform
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`auto-rotate, de—skew, perspective distortion correction, cropping, etc. Additionally, the mobile
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`device might also process the image to produce a bi—tona1 image for data extraction. In some
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`cases, the processing might be shared between the mobile device and the server.
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`[0051]
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`In step 712, the processing of a financial document using a mobile device is
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`completed. For example, when the server has confirmed that all necessary data can be extracted
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`from a received image, it might transmit a status message to the mobile device that transmitted
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`the image. Alternatively, if some necessary data cannot be extracted, the server may transmit a
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`"request for additional data. This request might include a request for an additional image. In
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`some cases, the request may be for data entered by a user, for example, an amount, e.g., of a
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`check, that might be entered using a key pad on the mobile communication device.
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`[0052]
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`In some embodiments, the quality of the image is determined at the mobile
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`device. In this way the number of requests from the server for additional images might be
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`reduced. The request may come directly from the mobile device. This may allow for the request
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`to be more quickly determined and may allow a user to take an additional image within a shorter
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`time from the earlier image. This may mean, for example, that the user is still physically close to
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`the document and is still holding the communication device. This might make it easier to retake
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`an image. If the image quality processing occurs at a server it might take longer to determine
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`that the image quality is acceptable and communicate that information back to a user. This may
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`mean the user is no longer near the document or has started performing another task. It will be
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`understood, however, that in some embodiments, a server based implementation might be
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`employed to off-load processing demands from the mobile device. Additionally, in some cases it
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`might be as quick or quicker than a system that uses the mobile communication device to process
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`an image to determine image quality.
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`[0053]
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`Figure 8 is a flowchart illustrating an example method in accordance with the
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`systems and methods described herein. Referring to Figure 8, a method for generating a
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`recognition-friendly bi-tonal image is discussed. In step 800, an image is converted to grayscale.
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`In some embodiments, the information needed to process a document (such as a check or a
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`payment coupon) might be preserved when, for example, a 24 bit per pixel color image is
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`converted to an 8 bit per pixel gray scale image. In other words, in some cases, no needed
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`information is lost during the conversion from a color picture to a bi-tonal image or gray scale
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`image.
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`[0054]
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`In step 802, a method similar to a Hough Transform might be used to detect a
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`quadrangle on, for example, the grayscale image. The Hough Transform is a technique used in
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`image analysis for digital image processing, including, for example, feature extraction and
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`computer vision. The technique may be used to find imperfect instances of one or more objects
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`using a Voting procedure. For example, the Hough Transform may be used to isolate features of
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`a particular shape within an image. Generally, the desired features should be specified in some
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`parametric form. One common use for the Hough Transform is the detection of regular curves
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`such as lines, circles, ellipses, etc. Some embodiments may use a generalized Hough Transform.
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`Such a transform may be employed in applications where a simple analytic description of a
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`feature is not possible. In other embodiments a generalized Hough Algorithm might be used.
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`[0055]
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`The Hough Transform Algorithm uses an array, called an accumulator, to detect
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`the existence of a line. For example, using the generalized equation of a line (y = mx + b) the
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`linear Hough Transform has two unknown parameters: m and b. An accumulator array for such
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`as transform would comprise quantized values for In and b. For each pixel and its neighborhood,
`the Hough Transform algorithm may determine an edge occurs at that pixel. If the edge has
`
`occurred the parameters of that line at that pixel may be calculated.
`
`[0056]
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`When a line is determined the accumulator's bin can be increased in value. The
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`most likely lines may be extracted, and their approximate geometric d