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

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

receiving an image which includes a vehicle identification number (VIN);

making a color assumption with respect to the VIN;

preprocessing the image based on the color assumption;

segmenting the image to identify candidate text strings that may include the VIN;

performing an optical character recognition (OCR) using an OCR engine on the candidate text strings;

validating the candidate text strings;

if a candidate test string is validated, outputting a confirmed VIN value based on the validated candidate test string; and

if a candidate test string fails the validation, post-processing the failed candidate text string, which includes using a different OCR engine to re-recognize some or all characters in the failed candidate test string.

2. The non-transitory computer readable medium of claim 1 , wherein preprocessing the image includes at least one of: converting the image to a grayscale based on the color assumption; and binarizing a grayscale image.

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

4. The non-transitory computer readable medium of claim 3 , wherein the redundancy test includes a mod11 rule test.

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

6. 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 generated the color conversion formula:

BR ( 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,

BR(P)—the 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 the original color image.

7. 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:

receive an image which includes a vehicle identification number (VIN);

make a color assumption with respect to the VIN;

preprocess the image based on the color assumption;

segment the image to identify candidate text strings that may include the VIN;

perform an optical character recognition (OCR) using an OCR engine on the candidate text strings;

validate the candidate text strings;

if a candidate test string is validated, output a confirmed VIN value based on the validated candidate test string; and

if a candidate test string fails the validation, post-processing the failed candidate text string, which includes using a different OCR engine to re-recognize some or all characters in the failed candidate test string.

8. The system of claim 7 , wherein preprocessing the image includes at least one of: converting the image to a grayscale based on the color assumption; and binarizing a grayscale image.

9. The system of claim 7 , wherein validating the candidate text strings comprises performing a redundancy test.

10. The system of claim 9 , wherein the redundancy test includes a mod11 rule test.

11. The system of claim 7 , wherein performing an optical character recognition on the candidate text strings produces ACII text strings for each candidate text string.

12. The system of claim 7 , wherein the color assumption is represented by set of three weights WR, WG and WB (WR+WG +WB=1.0) to generated the color conversion formula:

BR ( 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,

BR(P)—the 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 the original color image.

13. A method for identifying a field in an image of a non-document, comprising:

receiving an image which includes a vehicle identification number (VIN);

making a color assumption with respect to the VIN;

preprocessing the image based on the color assumption;

segmenting the image to identify candidate text strings that may include the VIN;

performing an optical character recognition (OCR) using an OCR engine on the candidate text strings;

validating the candidate text strings;

if a candidate test string is validated, outputting a confirmed VIN value based on the validated candidate test string; and

if a candidate test string fails the validation, post-processing the failed candidate text string, which includes using a different OCR engine to re-recognize some or all characters in the failed candidate test string.

14. The method of claim 13 , wherein preprocessing the image includes at least one of: converting the image to a grayscale based on the color assumption; and binarizing a grayscale image.

15. The method of claim 13 , wherein validating the candidate text strings comprises performing a redundancy test.

16. The method of claim 15 , wherein the redundancy test includes a mod11 rule test.

17. The method of claim 13 , wherein performing an optical character recognition on the candidate text strings produces ACII text strings for each candidate text string.

18. The method of claim 13 , wherein the color assumption may be represented by set of three weights WR, WG and WB (WR+WG+WB=1.0) to generated the color conversion formula:

BR ( 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,

BR(P)—the 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 the original color image.