Hybrid feature-based and template matching optical character recognition system

• US 5,077,805 A
• Filed: 05/07/1990
• Issued: 12/31/1991
• Est. Priority Date: 05/07/1990
• Status: Expired due to Term

• Current Assignee: Eastman Kodak Company
• Original Assignee: Eastman Kodak Company
• Inventors: Tan, Hin-Leong
• Primary Examiner(s): Moore, David K.
• Assistant Examiner(s): Couso, Jose L.
• Application Number:
  US07/520,310
• Time in Patent Office: 603 Days
• Field of Search: 382/7, 382/9, 382/30, 382/34, 382/48, 382/61, 235/379
• US Class Current: 382/137
• CPC Class Codes: G06V 30/2504 Coarse or fine approaches, e.g. resolution of ambiguities or multiscale approaches

Abstract

A feature-based character recognition identification and confidence level are determined for an unknown symbol. If the confidence level is within an intermediate range, the feature-based identification is confirmed by matching the unknown character with a reference template corresponding to the feature-based identification. If the confidence level is below the intermediate range, template matching character recognition is substituted in place of the feature-based identification. If the template matching recognition identifies more than one symbol, corresponding templates from a second set of templates having thicker character strokes are employed to resolve the ambiguity.

Claims

1. An optical character recognition system which uses (a) a predetermined set of reference vectors representing in a vector basis set of features a set of symbols and (b) a first set of reference templates corresponding to said set of symbols, said system comprising:

character location means for locating a first region around an individual character image among a plurality of character images in a document image;

means for generating a character vector representing in said vector basis set of features said individual character;

means for determining which one of said reference vectors has a smallest distance from said character vector and which one of said reference vectors has a next-smallest distance from said character vector;

feature-based character recognition means for computing a confidence value based upon a comparison of said smallest distance and said next-smallest distance and for associating said character image with one of said symbols corresponding to the reference vector having said smallest distance from said character vector whenever said confidence value is at least a first predetermined threshold; and

template matching character recognition means responsive whenever said confidence value is below said first predetermined threshold for searching within said first region in said image around said one individual character image for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of one of said first set of reference templates corresponding to said set of symbols.

2. The system of claim 1 wherein said template matching character recognition means comprises means responsive upon failing to find said matching pattern of "ON" pixels within said first region for searching within a second region larger than said first region surrounding said character image for said pattern of "ON" pixels.

3. The system of claim 1 wherein said template matching character recognition means comprises means responsive whenever said confidence value is above said first threshold but less than a second predetermined threshold for searching in said first region around said character image for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of a particular one of said reference templates corresponding to the symbol identified by said feature-based character recognition means.

4. The system of claim 1 wherein said template matching character recognition means comprises means responsive whenever said character image matches more than one of said reference templates, for searching for a pattern of "ON" pixels in said first region matching to within at least a threshold number "ON" pixels of a second set of reference templates characterized by character strokes thicker than those of said first set of reference templates and corresponding only to those reference templates of said first set for which a match was found with said character image.

5. The system of claim 1 wherein said document image is characterized by a set of character images whose locations are established with reference to an image of a predetermined fiducial symbol in said set of symbols, said fiducial symbol image being located nearest one predetermined boundary of said document image, wherein:

said character location means comprises means for locating a third region in said document image around said fiducial symbol image;

said feature based character recognition means comprises means responsive upon said character location means locating said third region for computing a confidence value associated with said fiducial symbol image and one set of reference features corresponding to said fiducial symbol; and

said template matching character recognition means comprises means responsive whenever said confidence value associated with said fiducial symbol image is below said first threshold for searching in said third region for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of one reference template of said first set of reference templates corresponding to said fiducial symbol.

6. The system of claim 5 wherein said template matching character recognition means is further responsive whenever said matching pattern of "ON" pixels is not found in said third region for searching in a fourth region around said fiducial symbol image larger than said third region for said pattern of "ON" pixels.

7. The system of claim 5 wherein said set of characters are MICR characters and said document image represents a bank check.

8. The system of claim 1 wherein said predetermined set of features comprises a set of N-dimensional reference vectors representing said set of symbols and corresponding to locations of "ON" pixels in the images of said set of symbols, and wherein said feature based character recognition means comprises:

means for measuring the vector distances between an N-dimensional vector corresponding to said character image and each one of said set of N-dimensional reference vectors; and

means for computing the ratio between the two shortest ones of said vector distances associated with said character image.

9. An optical character recognition method which uses (a) a predetermined set of reference vectors representing in a vector basis set of features a set of symbols and (b) a first set of reference templates corresponding to said set of symbols, comprising:

locating a first region around an individual character image among a plurality of character images in a document image;

generating a character vector representing in said vector basis set of features said individual character;

determining which one of said reference vectors has a smallest distance from said character vector and which one of said reference vectors has a next-smallest distance from said character vector;

computing a confidence value based upon a comparison of said smallest distance and said next-smallest distance and associating said character image with one of said symbols corresponding to the reference vector having said smallest distance from said character vector whenever said confidence value is at least a first predetermined threshold; and

wherever said confidence value is below said first predetermined threshold, first searching within said first region in said image around said one individual character image for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of one of said first set of reference templates corresponding to said set of symbols.

10. The method of claim 9 further comprising searching within a second region larger than said first region surrounding said character image for said pattern of "ON" pixels upon said first searching step failing to find said matching pattern of "ON" pixels within said first region.

11. The method of claim 9 further comprising the step of searching in said first region around said character image for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of a particular one of said reference templates representing the symbol corresponding to said confidence value whenever said confidence value is above said first threshold but less than a second predetermined threshold.

12. The method of claim 9 further comprising searching for a pattern of "ON" pixels in said first region matching to within at least a threshold number "ON" pixels of a second set of reference templates characterized by character strokes thicker than those of said first set of reference templates and corresponding only to those reference templates of said first set for which a match was found with said character image whenever said character image matches more than one of said first set of reference templates.

13. The method of claim 9 wherein said document image is characterized by a set of character images whose locations are established with reference to an image of a predetermined fiducial symbol in said set of symbols, said fiducial symbol image being located nearest one predetermined boundary of said document image, wherein said character locating step comprises:

locating a third region in said document image around said fiducial symbol image;

computing a confidence value associated with said fiducial symbol image and one set of reference features corresponding to said fiducial symbol; and

whenever said confidence value associated with said fiducial symbol image is below said first threshold, searching in said third region for a pattern of "ON" pixels matching to within at least a threshold number "ON" pixels of one reference template of said first set of reference template corresponding to said fiducial symbol.

14. The method of claim 13 for searching in a fourth region around said fiducial symbol image larger than said third region for said pattern of "ON" pixels whenever said matching pattern of "ON" pixels is not found in said third region.

15. The method of claim 13 wherein said set of characters are MICR characters and said document image represents a bank check.

16. The method of claim 9 wherein said predetermined set of features comprises a set of N-dimensional reference vectors representing said set of symbols and corresponding to locations of "ON" pixels in the images of said set of symbols, and wherein said computing step comprises:

measuring the vector distances between an N-dimensional vector corresponding to said character image and each one of said set of N-dimensional reference vectors; and

computing the ratio between the shortest and second shortest vector distances associated with said character image.