Image analysis – Pattern recognition – On-line recognition of handwritten characters
Reexamination Certificate
2007-02-06
2007-02-06
Mariam, Daniel (Department: 2624)
Image analysis
Pattern recognition
On-line recognition of handwritten characters
C382S186000
Reexamination Certificate
active
10374482
ABSTRACT:
A character recognizer recognizes a handwritten input character. A sequence of points in two dimensional space representative of a stroke trajectory forming the input character is gathered. An input Directed Acyclic Graph is built with nodes representative of singular points at the beginning, end, and along the trajectory of the input character and with edges between nodes representative of an edge trajectory formed by the sequence of points of the input character between the singular points. Each edge in the input graph is described based on the shape, orientation and pen lift of the edge trajectory that the edge represents. The input graph is evaluated against model graphs, which are also Directed Acyclic Graphs, for all possible characters to find a path through a model graph that produces a best path similarity score with a corresponding path through the input graph. The input character is identified as an answer character represented by the model graph producing the best path similarity score. Each model graph for a reference character has nodes representative of singular points at the beginning, end, and along the stroke trajectory of the reference character. Edges between nodes in the model graph are representative of an edge trajectory formed by the sequence of points of the reference character between the singular points. Further each edge of the model graph has an “i” vector, a “j” vector and a “k” vector, and the “i,j,k” vectors indicate similarity values associated with the edge trajectory of the reference character and typical shapes, shape rotation and pen lift, respectively.
REFERENCES:
patent: 5303312 (1994-04-01), Comerford et al.
patent: 5742705 (1998-04-01), Parthasarathy
patent: 5889889 (1999-03-01), Sinden
patent: 5903668 (1999-05-01), Beernink
patent: 6052481 (2000-04-01), Grajski et al.
patent: 6307964 (2001-10-01), Lin et al.
patent: 7003158 (2006-02-01), Bennett et al.
Christopher G. Small, “The Statistical Theory of Shape,” Springer-Verlag, New York, 1996, pp. 1-26.
I-Jong Lin and S.Y. Kung, “Coding and Comparison of DAGs as a Novel Neural Structure with Applications to On-Line Handwriting Recognition,” IEEE Transactions on Signal Processing, 45(11):2701-8, Nov. 1997.
I-Jong Lin and S.Y. Kung, “A Novel Learning Method by Structural Reduction of DAGs for On-Line OCR Applications,” International Conference on Acoustic, Speech, and Signal Processing, 1998.
D. Kanevsky, “A Generalization of the Baum Algorithm to Functions on Non-Linear Manifolds,” In Proc. International Conference on Acoustic, Speech and Signal Processing, vol. 1, p. 473-476, 1995.
L. Rabinber, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of IEEE, 77(2):257-86, Feb. 1989.
Yen-Kuang Chen, “Neural Structure of DAGs,” www.ee.Princeton.edu/˜ijonglin/spaces
ode16.html, Sep. 25, 1998.
PCT International Search Report for PCT/US2004/005272.
Bellegarda, E.J. et al., “On-Line Handwritten Character Recognition Using Parallel Neural Networks,” proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, Conf. 19, Apr. 19, 1994, pp. II-605-II to II-608.
“Method for Supporting Stroke Variations in Online Handwritten Character Recognition,” IBM Technical Disclosure Bulletin, IBM Corp., New York, vol. 35, No. 5, Oct. 1, 1992, pp. 358-359.
I-Jong Lin et al., “Coding and Comparison of DAGs as a novel neural structure with applications to on-line handwriting recognition,” IEEE Trans. Signal Process, IEEE Transactions on Signal Processing, IEEE, vol. 45, No. 11, Nov. 1997, pp. 2701-2708.
Bagotskaya Natalia
Lossev Ilia
Blakely & Sokoloff, Taylor & Zafman
EverNote Corp.
Mariam Daniel
LandOfFree
On-line handwriting recognizer does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with On-line handwriting recognizer, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On-line handwriting recognizer will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3856289