MULTIPLE IMAGE INPUT FOR OPTICAL CHARACTER RECOGNITION PROCESSING SYSTEMS AND METHODS

• US 20070211942A1
• Filed: 11/15/2006
• Published: 09/13/2007
• Est. Priority Date: 03/07/2006
• Status: Active Grant

• Current Assignee: Ancestry.com Operations Inc. (Ancestry.com Incorporated)
• Original Assignee: The Generations Network, Inc.
• Inventors: Curtis, Donald B., Reid, Shawn
• Granted Patent: US 7,734,092 B2
• Time in Patent Office: Days
• US Class Current: 382/181
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data

Abstract

A method of processing an image includes receiving a digital version of the image, processing the digital version of the image through at least two binarization processes to thereby create a first binarization and a second binarization, and processing the first binarization through a first optical character recognition process to thereby create a first OCR output file. Processing the first binarization through a first optical character recognition process includes compiling first metrics associated with the first OCR output file. The method also includes processing the second binarization through the first optical character recognition process to thereby create a second OCR output file. Processing the second binarization through the first optical character recognition process includes compiling second metrics associated with the second OCR output file. The method also includes using the metrics, at least in part, to select a final OCR output file from among the OCR output files.

Claims

1. A method of processing an image, comprising:

receiving a digital version of the image;

processing the digital version of the image through at least two binarization processes to thereby create a first binarization and a second binarization;

processing the first binarization through a first optical character recognition process to thereby create a first OCR output file, wherein processing the first binarization through a first optical character recognition process comprises compiling first metrics associated with the first OCR output file;

processing the second binarization through the first optical character recognition process to thereby create a second OCR output file, wherein processing the second binarization through the first optical character recognition process comprises compiling second metrics associated with the second OCR output file; and

using the metrics, at least in part, to select a final OCR output file from among the OCR output files.

2. The method of claim 1, further comprising:

processing the first binarization through a second optical character recognition process to thereby create a third OCR output file, wherein processing the first binarization through a second optical character recognition process comprises compiling third metrics associated with the third OCR output file; and

processing the second binarization through the second optical character recognition process to thereby create a fourth OCR output file, wherein processing the second binarization through the second optical character recognition process comprises compiling fourth metrics associated with the fourth OCR output file.

3. The method of claim 1, wherein the binarization processes are selected from a group consisting of:

clustering;

global-thresholding; and

adaptive thresholding.

4. The method of claim 1, wherein the first and second optical character recognition processes comprise the same optical character recognition process.

5. The method of claim 1, wherein the first and second optical character recognition processes comprise different optical character recognition process.

6. The method of claim 1, wherein the metrics associated with a particular output file comprise one or more selections from a group consisting of:

a number of characters recognized in the particular output file;

a number of dictionary words in the particular output file;

a number of unknown words in the particular output file;

a per-character confidence level in the particular output file;

a per-word confidence level in the particular output file;

a per-image confidence level in the particular output file;

a degree of agreement between the particular output file and other output files;

which binarization and OCR process produced the particular output file; and

a measure of historical accuracy associated with the particular binarization/OCR combination that produced the particular output file.

7. The method of claim 1, creating the digital version of the image from a physical version of the image.

8. A method of optically recognizing characters in an image, comprising:

creating multiple binarizations of the image using different binarization techniques;

presenting each binarization to an optical character recognition (OCR) engine to produce OCR output file for each binarization;

developing metrics relating to each OCR output file; and

using the metrics, at least in part, to select a final OCR output file from among the OCR output files.

9. The method of claim 8, wherein the different binarization techniques comprise one or more selections from a group consisting of:

clustering;

global-thresholding; and

adaptive thresholding.

10. The method of claim 8, wherein presenting each binarization to an OCR engine comprises presenting each binarization to a different OCR engine.

11. The method of claim 8, wherein the metrics relating to a particular OCR output file comprise one or more selections from a group consisting of:

a number of characters recognized in the particular OCR output file;

a number of dictionary words in the particular OCR output file;

a number of unknown words in the particular OCR output file;

a per-character confidence level in the particular output file;

a per-word confidence level in the particular output file;

a per-image confidence level in the particular output file;

a degree of agreement between the particular OCR output file and other OCR output files;

which binarization and OCR process produced the particular OCR output file; and

a measure of historical accuracy associated with the particular binarization/OCR combination that produced the particular OCR output file.

12. An optical character recognition system, comprising:

at least two binarization processes configured to convert grayscale images to bitonal images;

at least one optical character recognition process configured to process bitonal images into final output files having characters therein;

a metrics generation process configured to analyze output files and produce metrics associated therewith;

a voting process configured to select a final output file from among the output files based on the metrics;

a storage arrangement configured to store final output files and serve the information therein to users; and

at least one processor programmed to execute the at least one optical character recognition processes, the at least one optical character recognition process, metrics generation process, and the voting process.

13. The optical character recognition system of claim 12, wherein the at least two binarization processes comprise at least one selection from a group consisting of:

clustering;

global-thresholding; and

adaptive thresholding.

14. The optical character recognition system of claim 12, wherein the metrics associated with a particular output file comprise one or more selections from a group consisting of:

a number of characters recognized in the particular output file;

a number of dictionary words in the particular output file;

a number of unknown words in the particular output file;

a per-character confidence level in the particular output file;

a per-word confidence level in the particular output file;

a per-image confidence level in the particular output file;

a degree of agreement between the particular output file and other output files;

which binarization and OCR process produced the particular output file; and

a measure of historical accuracy associated with the particular binarization/OCR combination that produced the particular output file.