Scalable hash-based character recognition

Details Scalable hash-based character recognition Scalable hash-based character recognition

2005-01-28

2010-02-16

Mariam, Daniel G (Department: 2624)

Image analysis

Pattern recognition

C382S187000, C382S209000, C382S306000, C704S010000, C703S003000, C717S170000

Reexamination Certificate

active

07664323

ABSTRACT:
The subject invention leverages a scalable character glyph hash table to provide an efficient means to identify print characters where the character glyphs are identical over independent presentation. The hash table allows for quick determinations of glyph meta data as, for example, a pre-filter to traditional OCR techniques. The hash table can be trained for a particular environment, user, language, character set (e.g., alphabet), document type, and/or specific document and the like. This permits substantial flexibility and increases in speed in identifying unknown glyphs. The hash table itself can be composed of single or multiple tables that have a specific optimization purpose. In one instance of the subject invention, traditional OCR techniques can be utilized to update the hash tables as needed based on glyph frequency. This keeps the hash tables from growing by limiting updates that reduce its performance, while adding frequently determined glyphs to increase the pre-filter performance.

REFERENCES:
patent: 5392363 (1995-02-01), Fujisaki et al.
patent: 5469354 (1995-11-01), Hatakeyama et al.
patent: 6621941 (2003-09-01), Syeda-Mahmood et al.
patent: 7124408 (2006-10-01), Parthasarathy et al.
patent: 2002/0123882 (2002-09-01), Mohammed
patent: 2003/0113021 (2003-06-01), Shiotani
patent: 2003/0193994 (2003-10-01), Stickler
patent: 2004/0101198 (2004-05-01), Barbara
patent: 2005/0198468 (2005-09-01), Rubin et al.
patent: 2006/0005247 (2006-01-01), Zhang et al.
patent: 2006/0112121 (2006-05-01), McKenney et al.
Thomas H. Cormen, et al., Introduction to Algorithms (MIT Electrical Engineering and Computer Science), 1990, MIT Press.
Benjamin B. Kimia, Shape Representation for Image Retrieval, at Chapter 13 of Image Databases : Search and Retrieval Of Digital Imagery, Editor(s): Vittorio Castelli, Lawrence D. Bergman, Apr. 2002, John Wiley & Sons.
Manuel J. Fonseca, et al., Retrieving ClipArt Images by Content, International Conference on Image and Video Retrieval (CIVR'04), Jul. 2004, Dublin, Ireland.
E.G. M. Petrakis, et al., Similarity Searching in Large Image Databases, 1995, Technical Report 3388, Department of Computer Science, University of Maryland.
B. Kaliski, RFC 1319—The MD2 Message-Digest Algorithm, Apr. 1992, RSA Data Security, Inc.
R. Rivest, RFC 1320—The MD4 Message-Digest Algorithm, Apr. 1992, MIT Laboratory for Computer Science and RSA Data Security, Inc.
R. Rivest, RFC 1321—The MD5 Message-Digest Algorithm, Apr. 1992, MIT Laboratory for Computer Science and RSA Data Security, Inc.
D. Eastlake, 3rd, et al., RFC 3174—US Secure Hash Algorithm 1 (SHA1), Sep. 2001, Motorola (Eastlake) and Cisco Systems (Jones).

Affiliated with

Also associated with

Rating

Scalable hash-based character recognition does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Scalable hash-based character recognition, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Scalable hash-based character recognition will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4164238