Methods for mobile image capture of vehicle identification numbers in a non-document

Patent metadata

Field	Value
Publication number	US-10095947-B2
Application number	US-201715714362-A
Country	US
Kind code	B2
Filing date	Sep 25, 2017
Priority date	Mar 15, 2013
Publication date	Oct 9, 2018
Grant date	Oct 9, 2018

Abstract

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

First claim

What is claimed is:

1. A non-transitory computer readable medium containing instructions which, when executed by a computer, perform a process comprising:
capturing an image which includes a vehicle identification number (VIN);
making a color assumption with respect to the VIN;
identifying candidate text strings that may include the VIN;
performing an optical character recognition (OCR) on the candidate text strings;
sending the candidate text strings for validation; and
in response to the candidate test string being validated, receiving a confirmed VIN value for the validated candidate text strings.

2. The non-transitory computer readable medium of claim 1 , wherein validating the candidate text strings comprises performing a redundancy test.

3. The non-transitory computer readable medium of claim 2 , wherein the redundancy test includes a mod 11 rule test.

4. The non-transitory computer readable medium of claim 1 , wherein performing an optical character recognition on the candidate text strings produces ASCII text strings for each candidate text string.

5. The non-transitory computer readable medium of claim 1 , wherein the color assumption may be represented by set of three weights WR, WG and WB (WR+WG+WB=1.0) to generate a color conversion formula:
B ( P )=( R ( P )* WR+G ( P )* WG+B ( P )* WB )/3, where
P=P(X, Y)—an arbitrary pixel on the image represented by its X and Y-coordinates,
B(P)—a computed brightness value of pixel P on output grayscale image, and
R(P), G(P) and B(P)—Red, Green and Blue color value of pixel P on an original color image.

6. The non-transitory computer readable medium of claim 1 , further comprising post processing candidate text strings that fail validation.

7. The non-transitory computer readable medium of claim 6 , wherein the post processing comprises using second alternatives of OCR recognition for OCR engines that provide multiple recognition results per character.

8. A system for identifying a field in an image of a non-document, comprising:
a memory configured to store the image; and
a processor coupled with the memory, the processor configured to:
capture an image which includes a vehicle identification number (VIN);
make a color assumption with respect to the VIN;
identify candidate text strings that may include the VIN;
perform an optical character recognition (OCR) on the candidate text strings;
send the candidate text strings for validation; and
in response to a candidate test string being validated, receive a confirmed VIN value for the validated candidate text strings.

9. The system of claim 8 , wherein validating the candidate text strings comprises performing a redundancy test.

10. The system of claim 9 , wherein the redundancy test includes a mod 11 rule test.

11. The system of claim 8 , wherein performing an optical character recognition on the candidate text strings produces ASCII text strings for each candidate text string.

12. The system of claim 8 , wherein the color assumption is represented by set of three weights WR, WG and WB (WR+WG+WB=1.0) to generate a color conversion formula:
B ( P )=( R ( P )* WR+G ( P )* WG+B ( P )* WB )/3, where
P=P(X, Y)—an arbitrary pixel on the image represented by its X and Y-coordinates,
B(P)—a computed brightness value of pixel P on output grayscale image, and
R(P), G(P) and B(P)—Red, Green and Blue color value of pixel P on an original color image.

13. The system of claim 8 , wherein the process is further configured to post process candidate text strings that fail validation.

14. The system of claim 13 , wherein the post processing comprises using second alternatives of OCR recognition for OCR engines that provide multiple recognition results per character.

15. A method for identifying a field in an image of a non-document, comprising:
capturing an image which includes a vehicle identification number (VIN);
making a color assumption with respect to the VIN;
identifying candidate text strings that may include the VIN;
performing an optical character recognition (OCR) on the candidate text strings;
sending the candidate text strings for validation; and
in response to a candidate test string being validated, receive a confirmed VIN value for the validated candidate text strings.

16. The method of claim 15 , wherein validating the candidate text strings comprises performing a redundancy test.

17. The method of claim 16 , wherein the redundancy test includes a mod 11 rule test.

18. The method of claim 15 , wherein performing an optical character recognition on the candidate text strings produces ASCII text strings for each candidate text string.

19. The method of claim 15 , wherein the color assumption may be represented by set of three weights WR, WG and WB (WR+WG+WB=1.0) to generate a color conversion formula:
B ( P )=( R ( P )* WR+G ( P )* WG+B ( P )* WB )/3, where
P=P(X, Y)—an arbitrary pixel on the image represented by its X and Y-coordinates,
B(P)—a computed brightness value of pixel P on the output grayscale image, and
R(P), G(P) and B(P)—Red, Green and Blue color value of pixel P on an original color image.

20. The method of claim 15 , further comprising post processing candidate text strings that fail validation.

Assignees

• Mitek Systems Inc

Inventors

• Nepomniachtchi Grigori
• Kotovich Nikolay

Classifications

• G06V30/153

  using recognition of characters or words · CPC title

• G06V30/40 Primary

  Document-oriented image-based pattern recognition · CPC title

• G06V10/98

  Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns · CPC title

• G06V10/22

  by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition · CPC title

• G06V10/28

  Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns · CPC title