SYSTEMS AND METHODS FOR RECEIPT-BASED MOBILE IMAGE CAPTURE

• US 20140268250A1
• Filed: 03/17/2014
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Mitek Systems Incorporated
• Original Assignee: Mitek Systems Incorporated
• Inventors: Nepomniachtchi, Grigori, Kotovich, Nikolay
• Application Number:
  US14/217,139
• Publication Number:
  US 20140268250A1
• Time in Patent Office: Days
• US Class Current: 358/462
• CPC Class Codes: G06V 10/22 by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
• CPC Class Codes: G06V 30/40 Document-oriented image-based pattern recognition
• CPC Class Codes: H04N 1/00307 with a mobile telephone apparatus constructional features of portable telephone sets H04M1/0202; mobile radio systems H04W84/00

Abstract

Systems and methods of capturing data from mobile images of receipts implemented are provided herein. One of the most important tasks behind the mobile receipt capture technology is understanding and utilizing category-specific rules in the form of known document sizes, relationships between different document fields, etc. For example, knowledge that many receipts have 3 inch widths helps to alter an image to restore the actual size of a receipt, which in turn improves a printing function and, most importantly, accuracy of content extraction such as optical character recognition.

Claims

1. A computer readable medium containing instructions which, when executed by a computer, perform a process comprising:

receiving an image of a receipt;

preprocessing the image of the receipt in preparation for data extraction;

identifying the size of the receipt based on the image of the receipt;

resizing the image of the receipt when it is determined based on the size identification that resizing is necessary;

identifying at least one field on the receipt;

extracting a set of data from the at least one identified field; and

displaying the extracted set of data to a user.

2. The computer readable medium of claim 1, wherein the preprocessing comprises creating grayscale snippets of the receipt.

3. The computer readable medium of claim 2, wherein the snippets are high contrast snippets.

4. The computer readable medium of claim 2, wherein the snippets are low contrast snippets.

5. The computer readable medium of claim 1, wherein the preprocessing comprises creating bitonal snippets of the receipt.

6. The computer readable medium of claim 1, wherein identifying the size of the receipt comprises detecting vertical text within the image.

7. The computer readable medium of claim 6, wherein identifying the size of the receipt further comprises, when vertical text is detected, then rotating the image by 90 degrees.

8. The computer readable medium of claim 6, wherein connected components analysis is used to identify the size of the receipt and rotate the receipt if necessary.

9. The computer readable medium of claim 1, wherein the process further comprises detecting upside-down text in the image of the receipt.

10. The computer readable medium of claim 9, wherein the image is rotated by 180 degrees, when upside down text is detected.

11. The computer readable medium of claim 8, wherein connected components analysis is used to detect upside sown text and rotate the image if necessary.

12. The computer readable medium of claim 1, wherein identifying the size of the receipt and resizing the image of the receipt is performed using connected components analysis in which all the connected components (CCs) are found in image.

13. The computer readable medium of claim 12, wherein the process further comprises performing a histogram analysis on CCs to detect the most frequent CC'"'"'s width.

14. The computer readable medium of claim 13, wherein the process further comprises, using additional logic to detect if two most frequent values are candidates for the size of regular and capitalize characters, when the histogram analysis produces more than one candidate.

15. The computer readable medium of claim 12, wherein a character width found in the connected components analysis is compared to expected width on 3″

receipts, and wherein if the width is determined to be approximately close to the expected width, then rescaling a grayscale or bitonal snippet of the image of the receipt using a known document width of 3″

.

16. The computer readable medium of claim 12, wherein a character width found in the connected component analysis is compared to expected width on 11″

×

8.5″

receipts, and wherein if the width is approximately close to the expected width, then rescaling a grayscale or bitonal snippet of the image of the receipt using a known document width of 8.5″ and

known height of 11″

.

17. The computer readable medium of claim 1, wherein the at least on field on the receipt comprises at least one of:

a date, an address, an amount.

18. The computer readable medium of claim 17, wherein the date is identified and extracted by identifying month, day and year fields, parsing the data and determining whether the parsed data is an acceptable date.

19. The computer readable medium of claim 17, wherein the date is identified and extracted by identifying potential date fields, parsing the data, and assigning a format-based confidence value to each potential field based on the parsed data.

20. The computer readable medium of claim 17, wherein identifying the amount comprises performing at least one of keyword-based search and format-based search.