Handwriting recognition using a comparative neural network

Details Handwriting recognition using a comparative neural network Handwriting recognition using a comparative neural network

2005-06-10

2009-02-24

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

C706S015000, C706S048000, C382S159000

Reexamination Certificate

active

07496547

ABSTRACT:
Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual's previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual's handwriting that accurately represent the individual's writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.

REFERENCES:
patent: 2002/0150295 (2002-10-01), Kwok et al.
S. Günter. Multiple classifier systems in offline cursive handwriting recognition. PhD thesis, University of Bern, Jan. 2004. Electronic copy avaiable at google or www.iam.unibe.ch/˜fki/publications/public/sguenter.pdf Only p. 1-5 and 54 are included with this office action.
C. Dwork, R. Kumar, M. Naor, and D. Sivakumar. Rank aggregation revisited. 2001. Manuscript Electronic copy avaiable at google or www.eecs.harvard.edu/˜michaelm/CS222/rank2pdf Only p. 1 and 4 are included with this office action.
Akira Nakamura (“A Method to Accelerate Write Adaptation for On-Line Handwriting Recognition of a Large Character Set” IWFHR-9, Oct. 2004).
Matti Aksela (“Handwritten Character Recognition: A Palm-top Implementation and Adaptive Committee Experiments” thesis, May 2000), p. 1, 90-96 only.
Dwork et al (“Rank aggregation revisited”, 2001, Manuscript).
Akira Nakamura (“A Method to Accelerate Writer Adaptation for On-Line Handwriting Recognition of a Large Character Set” IWFHR-9 2004, submitted Apr. 5, 2004).
Dwork et al (“Rank aggregation revisited”, 2001, Manuscript).
IWFHR9.

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