SYSTEMS FOR MOBILE IMAGE CAPTURE AND REMITTANCE PROCESSING

• US 20110091092A1
• Filed: 10/15/2010
• Published: 04/21/2011
• Est. Priority Date: 01/18/2008
• Status: Active Grant

• Current Assignee: Mitek Systems Incorporated
• Original Assignee: Mitek Systems Incorporated
• Inventors: DeBello, James, Roach, Josh, Nepomniachtchi, Grigori
• Granted Patent: US 8,577,118 B2
• Time in Patent Office: Days
• US Class Current: 382/139
• CPC Class Codes: G06F 40/186 Templates
• CPC Class Codes: G06Q 20/042 characterized in that the payment protocol involves at least one cheque
• CPC Class Codes: G06Q 20/3276 using a pictured code, e.g. barcode or QR-code, being read by the M-device
• CPC Class Codes: G06Q 20/387 Payment using discounts or coupons relating to marketing G06Q30/02
• CPC Class Codes: G06T 5/80 Geometric correction
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/1475 Inclination or skew detection or correction of characters or of image to be recognised
• CPC Class Codes: G06V 30/162 Quantising the image signal
• CPC Class Codes: G06V 30/166 Normalisation of pattern dimensions
• CPC Class Codes: G06V 30/414 Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text
• CPC Class Codes: H04N 1/00244 with a server, e.g. an internet server fax-servers or the like for store and forward H04N1/324
• CPC Class Codes: H04N 1/00307 with a mobile telephone apparatus constructional features of portable telephone sets H04M1/0202; mobile radio systems H04W84/00
• CPC Class Codes: H04N 2101/00 Still video cameras
• CPC Class Codes: H04N 2201/001 Sharing resources, e.g. processing power or memory, with a connected apparatus or enhancing the capability of the still picture apparatus
• CPC Class Codes: H04N 2201/0084 Digital still camera

Abstract

The present invention relates to automated document processing and more particularly, to methods and systems for document image capture and processing using mobile devices. In accordance with various embodiments, methods and systems for document image capture on a mobile communication device are provided such that the image is optimized and enhanced for data extraction from the document as depicted. These methods and systems may comprise capturing an image of a document using a mobile communication device; transmitting the image to a server; and processing the image to create a bi-tonal image of the document for data extraction. Additionally, these methods and systems may comprise capturing a first image of a document using the mobile communication device; automatically detecting the document within the image; geometrically correcting the image; binarizing the image; correcting the orientation of the image; correcting the size of the image; and outputting the resulting image of the document.

Claims

1. A computer implemented method for mobile image capture and remittance processing, where one or more processors are programmed to perform steps comprising:

receiving a mobile image of a remittance coupon captured using a mobile device;

detecting the remittance coupon within the mobile image of the remittance coupon;

generating a document subimage that includes a portion of the mobile image that corresponds to the remittance coupon;

geometrically correcting the mobile document image of the remittance coupon to generate a geometrically corrected image;

processing the geometrically corrected image to generate a processed image;

executing the one or more mobile image quality assurance tests on the processed image to assess the quality of the processed image; and

executing one or more remittance processing steps on the processed image if the processed image passes the mobile image quality assurance tests.

2. The method of claim 1, wherein processing the geometrically corrected to generate a processed image further comprises:

performing a dewarping operation to correct warping in the geometrically corrected image.

3. The method of claim 1, wherein processing the geometrically corrected image to generate a processed image further comprises:

performing a binarization operation on the geometrically corrected image to generate a bitonal image of the remittance coupon.

4. The method of claim 3, wherein processing the geometrically corrected image to generate a processed image further comprises:

identifying a code line from the remittance coupon in the bitonal image of the remittance coupon.

5. The method of claim 4, wherein processing the geometrically corrected image to generate a processed image further comprises:

determining a size for the code line from the bitonal image of the remittance coupon to determine a scaling factor.

6. The method of claim 5, wherein processing the geometrically corrected image to generate a processed image further comprises:

performing a second geometric correction operation on the mobile document subimage using the scaling factor to generate a scaled geometrically corrected image.

7. The method of claim 5, wherein performing a second geometric correction operation on the mobile document subimage further comprises:

identifying an orientation of the remittance coupon in the mobile document subimage; and

correcting the orientation of the mobile document subimage to be in a landscape orientation if the remittance coupon was not in a landscape orientation.

8. The method of claim 6, further comprising:

identifying an orientation of the remittance coupon in the mobile document subimage.

9. The method of claim 7, further comprising:

performing a binarization operation on the scaled geometrically corrected image to generate a second bitonal image of the remittance coupon.

10. The method of claim 1, wherein executing one or more remittance processing steps on the processed image if the processed image passes the mobile image quality assurance tests further comprises:

extracting text content from the processed image and identifying data in the extracted text content using a remittance coupon template.

11. The method of claim 10, wherein extracting text content from the processed image using a remittance coupon template further comprises:

selecting a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store; and

extracting text content from the processed image using an optical character recognition technique at locations specified by the remittance coupon template.

12. The method of claim 11, wherein selecting a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store further comprises:

analyzing the processed image using an optical character recognition technique to identify text content in the processed image; and

selecting a remittance coupon template from a plurality of remittance templates based on text content identified in the image.

13. The method of claim 11, wherein selecting a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store further comprises:

analyzing the processed image to identify a set of landmarks on the remittance coupon; and

selecting a remittance coupon template from a plurality of remittance templates based on the identified landmarks.

14. The method of claim 1, wherein executing one or more remittance processing steps on the processed image if the processed image passes the mobile image quality assurance tests further comprises:

extracting text content from the processed image and identifying data in the extracted text content using dynamic data capture.

15. The method of claim 14, wherein extracting text content from the processed image and identifying the extracted content using dynamic data capture further comprises:

locating data fields in the processed image using optical character recognition;

extracting data from data fields; and

analyzing the extracted data to identify the extracted data.

16. The method of claim 15, wherein locating data fields in the processed image using optical character recognition further comprises:

matching a set of keywords to text content identified in the processed image, wherein the set of keywords is associated with a particular type of data found on a remittance coupon;

locating text proximate to a keyword identified in the processed image; and

associating the text proximate to the keyword with the type of data associated with the keyword.

17. The method of claim 15, wherein locating data fields in the processed image using optical character recognition further comprises:

matching a format associated with a particular type of data found on a remittance coupon to a format of text content identified in the processed image; and

associating the matched text with the type of data associated with the format.

19. The method of claim 16, wherein parsing the parsing data from the code line further comprises:

matching a portion of the code line to other text content extracted from the processed image of the remittance coupon to identify the portion of the code line.

20. The method of claim 15, wherein executing one or more remittance processing steps on the processed image if the processed image passes the mobile image quality assurance tests further comprises:

receiving a second mobile document image of the remittance coupon from the mobile device;

geometrically correcting the second mobile document image of the remittance coupon to generate a third geometrically corrected image using the processor of the mobile device;

processing the third geometrically corrected image at the server to generate a second processed image; and

locating data fields in the second processed image using optical character recognition;

comparing the data fields located in the second processed image to the data fields from the first processed image; and

selecting values for data fields that match in the first processed image and the second processed image.

18. The method of claim 18, wherein locating data fields in the processed image using optical character recognition further comprises:

identifying a code line in the processed image of the remittance coupon; and

parsing data from the code line and associating the parsed data with a data type.

21. A system for mobile image capture and remittance processing, comprising:

an image correction module configured to

receive a mobile image a mobile image of a remittance coupon captured using a mobile device;

detect the remittance coupon within the mobile image of the remittance coupon;

generating a document subimage that includes a portion of the mobile image that corresponds to the remittance coupon;

geometrically correct the mobile document image of the remittance coupon to generate a geometrically corrected image;

process the geometrically corrected image at the server to generate a processed image;

a test execution module configured to execute the one or more mobile image quality assurance tests on the processed image to assess the quality of the processed image and to provide the processed image to the remittance processing module if the processed image passes the mobile image quality assurance tests; and

a remittance processing module configured to execute one or more remittance processing steps on the processed image.

22. The system of claim 21 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

perform a dewarping operation to correct warping in the geometrically corrected image.

23. The system of claim 21 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

perform a binarization operation on the geometrically corrected image to generate a bitonal image of the remittance coupon.

24. The system of claim 23 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

identify a code line of the remittance coupon in the bitonal image of the remittance coupon.

25. The system of claim 24 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

determine a size for the code line from the bitonal image of the remittance coupon to determine a scaling factor.

26. The system of claim 25 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

perform a second geometric correction operation on the mobile document subimage using the scaling factor to generate a scaled geometrically corrected image.

27. The system of claim 25 wherein the image correction module being configured to process the geometrically corrected image at the server to generate a processed image is further configured to:

identify an orientation of the remittance coupon in the mobile document subimage; and

correct the orientation of the mobile document subimage to be in a landscape orientation if the remittance coupon was not in a landscape orientation.

28. The system of claim 26 wherein the image correction module is further configured to:

identify an orientation of the remittance coupon in the mobile document subimage.

29. The system of claim 27 wherein the image correction module is further configured to:

perform a binarization operation on the scaled geometrically corrected image to generate a second bitonal image of the remittance coupon.

30. The system of claim 21, wherein the remittance processing module is configured to:

extract text content from the processed image and identifying data in the extracted text content using a remittance coupon template.

31. The system of claim 30, wherein the remittance processing module being configured to extract text content from the processed image using a remittance coupon template is further configured to:

select a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store; and

extract text content from the processed image using an optical character recognition technique at locations specified by the remittance coupon template.

32. The system of claim 31, wherein the remittance processing module being configured to select a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store further is further configured to:

analyze the processed image using an optical character recognition technique to identify text content in the processed image; and

select a remittance coupon template from a plurality of remittance templates based on text content identified in the image.

33. The system of claim 31, wherein the remittance processing module being configured to select a remittance coupon template from a plurality of remittance templates in a remittance coupon template data store further is further configured to:

analyze the processed image to identify a set of landmarks on the remittance coupon; and

select a remittance coupon template from a plurality of remittance templates based on the identified landmarks.

34. The system of claim 21 wherein the remittance processing module being configured to executing one or more remittance processing steps on the processed image is further configured to:

extract text content from the processed image and identify data in the extracted text content using dynamic data capture.

35. The system of claim 34 wherein the remittance processing module being configured to extract text content from the processed image and identify the extracted content using dynamic data capture is further configured to:

locate data fields in the processed image using optical character recognition;

extract data from data fields; and

analyze the extracted data to identify the extracted data.

36. The system of claim 35 wherein the remittance processing module being configured to locate data fields in the processed image using optical character recognition is further configured to:

match a set of keywords to text content identified in the processed image, wherein the set of keywords is associated with a particular type of data found on a remittance coupon;

locate text proximate to a keyword identified in the processed image; and

associate the text proximate to the keyword with the type of data associated with the keyword.

37. The system of claim 35 wherein the remittance processing module being configured to locate data fields in the processed image using optical character recognition is further configured to:

match a format associated with a particular type of data found on a remittance coupon to a format of text content identified in the processed image; and

associate the matched text with the type of data associated with the format.

38. The system of claim 35 wherein the remittance processing module being configured to locate data fields in the processed image using optical character recognition is further configured to:

identify a code line in the processed image of the remittance coupon; and

parse data from the code line and associating the parsed data with a data type.

39. The system of claim 38 wherein the remittance processing module being configured to parse the parsing data from the code line is further configured to:

match a portion of the code line to other text content extracted from the processed image of the remittance coupon to identify the portion of the code line.

40. The system of claim 35 wherein the remittance processing module being configured to executing one or more remittance processing steps on the processed image is further configured to:

receiving a second mobile document image of the remittance coupon from the mobile device;

geometrically correcting the second mobile document image of the remittance coupon to generate a third geometrically corrected image using the processor of the mobile device;

processing the third geometrically corrected image at the server to generate a second processed image; and

locating data fields in the second processed image using optical character recognition;

comparing the data fields located in the second processed image to the data fields from the first processed image; and

selecting values for data fields that match in the first processed image and the second processed image.