Real-time recognition of mixed source text

• US 20070065003A1
• Filed: 09/21/2005
• Published: 03/22/2007
• Est. Priority Date: 09/21/2005
• Status: Abandoned Application

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Kellerman, Eduardo, Rundle, Alfred, Paradis, Rosemary
• Application Number:
  US11/232,260
• Publication Number:
  US 20070065003A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06V 30/1423 the instrument generating sequences of position coordinates corresponding to handwriting preprocessing or recognising digital ink G06V30/32
• CPC Class Codes: G06V 30/2455 Discrimination between machine-print, hand-print and cursive writing
• CPC Class Codes: G06V 30/36 Matching; Classification

Abstract

Methods and computer program products are disclosed for the real-time classification of text from a region of interest within an image sample. A feature extractor extracts feature data associated with a plurality of region features from the region of interest. The plurality of region features are selected as to minimize the time necessary for feature extraction. A neural network preclassifier selects one of a plurality of associated source classes for the region of interest according to the extracted feature data. A plurality of classification systems are each associated with one of the plurality of source classes. Each of the plurality of classification systems are operative to classify individual characters within the region of interest when the associated source class of the classification system is selected.

Claims

1. An optical character recognition (OCR) system for the real-time classification of text from a region of interest within an image sample, comprising:

a feature extractor that extracts feature data associated with a plurality of region features from the region of interest;

a neural network preclassifier that selects one of a plurality of associated source classes for the region of interest according to the extracted feature data; and

a plurality of classification systems, each of the plurality of classification systems being associated with one of the plurality of source classes and being operative to classify individual characters within the region of interest when the associated source class of the classification system is selected.

2. The system of claim 1, further comprising a global preprocessing component that identifies and segments the region of interest from the image sample.

3. The system of claim 2, a given classification system comprising a regional preprocessing component that segments the region of interest into individual characters.

4. The system of claim 1, the plurality of region features being selected as to minimize the time necessary for feature extraction.

5. The system of claim 1, the plurality of classification systems including a first classification system, the first classification system comprising a statistical classifier.

6. The system of claim 5, the plurality of classification systems including a second classification system, the second classification system comprising a plurality of classifiers arbitrated by a rule based system.

7. The system of claim 1, a given classification system comprising a feature extractor that extracts a set of character features from individual characters comprising the region of interest.

8. A mail sorting system incorporating the OCR system of claim 1, the image sample comprising a scanned envelope and the region of interest comprising an address block on the envelope.

9. A computer program product, implemented on a computer readable medium and operative in a data processing system, for the real-time classification of text within a region of interest, comprising:

a feature extraction component that extracts feature values associated with a plurality of features relating to the region of interest from an image sample;

a preclassifier that selects one of a plurality of associated source classes for the region of interest according to the extracted feature values; and

a plurality of classifiers, each of the plurality of classifiers being associated with one of the plurality of source classes and being operative to classify individual characters within the region of interest when the associated source class of the classifier is selected;

wherein the feature extractor and preclassifier are configured such that the feature extractor and the preclassifier can operate to select one of the plurality of associated source classes within a predetermined period of time.

10. The computer program product of claim 9, the predetermined period of time having a duration of less than fifty milliseconds.

11. The computer program product of claim 9, the preclassifier comprising a software simulation of an artificial neural network.

12. The computer program product of claim 9, wherein a first classifier from the plurality of classifiers is associated with machine printed text, such that machine printed characters are classified at the first classifier, and a second classifier of the plurality of classifiers is associated with hand written text, such that hand written characters are classified at the second classifier.

13. The computer program product of claim 9, wherein a third classifier of the plurality of classifiers is associated with machine script, such that machine script characters are classified at the third classifier.

14. A method for classifying text from a region of interest in real-time comprising:

identifying a region of interest within a scanned image;

extracting a plurality of feature values, associated with a plurality of region features, from the region of interest;

classifying the region of interest into one of a plurality of source classes at a neural network preclassifier according to the extracted feature values;

selecting one of a plurality of classification systems according to the source class associated with the region of interest; and

classifying individual characters within the region of interest at the selected classification system.

15. The method of claim 14, further comprising extracting feature data corresponding to a plurality of character features from each individual character, the individual characters being classified according to the feature data associated with the character features.

16. The method of claim 14, wherein the step of extracting a plurality of feature values comprises:

identifying regions of connected pixels;

determining at least one characteristic of each identified region;

combining sets of at least one spatially proximate identified region into combined blobs; and

determining at least one characteristic of each combined blob.

17. The method of claim 16, wherein the identified characteristic includes at least one of the width, length, and baseline width of identified regions.

18. The method of claim 16, further comprising the steps of:

calculating at least feature value from the determined at least one characteristic of the identified regions; and

calculating at least one feature value from the determined at least one characteristic of the combined blobs.

19. The method of claim 16, wherein the step of calculating at least feature value from the determined at least one characteristic of the identified regions comprises:

determining a most common height of the identified region; and

determining a number of identified regions having associated heights within a range associated with the most common height.

20. The method of claim 15, wherein the steps of extracting a plurality of feature values and classifying the region of interest into one of a plurality of source classes are performed within a predetermined period of time.