DocumentCode
286271
Title
Integrating segmentation and recognition in on-line cursive handwriting using error-correcting grammars
Author
Flann, Nicholas S.
Author_Institution
Dept. of Comput. Sci., Utah State Univ., Logan, UT, USA
fYear
1993
fDate
22-23 Apr 1993
Abstract
A grammar-based approach is given to recognizing online cursive handwriting that overcomes the inherent problems of variability in input size and the need to integrate segmentation and recognition. The input stream is represented as a sequence of uniform stroke descriptions that are processed by a mixture of neural-networks, each designed to recognize letters of different sizes. Words are then recognized by a best-first search over the space of all possible segmentations controlled by two grammars, one defining legal letter sequences, the other defining legal segmentations of the input stream. Results demonstrate that the method is effective at learning to recognize a variety of handwriting styles, with the one writer producing word error rates of 3.1% (with a dictionary of 1000 words) and 10.8% (with a dictionary of 10000 words)
Keywords
character recognition; grammars; learning (artificial intelligence); neural nets; word processing; best-first search; grammar-based approach; input stream; legal letter sequences; legal segmentations; neural-networks; online cursive handwriting; uniform stroke descriptions; word error rates;
fLanguage
English
Publisher
iet
Conference_Titel
Grammatical Inference: Theory, Applications and Alternatives, IEE Colloquium on
Conference_Location
Colchester
Type
conf
Filename
243132
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