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Writer-adaptation for on-line handwritten character recognition

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4 Author(s)
N. Matic ; AT&T Bell Lab., Holmdel, NJ, USA ; I. Guyon ; J. Denker ; V. Vapnik

The authors have designed a writer-adaptable character recognition system for online characters entered on a touch terminal. It is based on a Time Delay Neural Network (TDNN) that is pre-trained on examples from many writers to recognize digits and uppercase letters. The TDNN without its last layer serves as a preprocessor for an optimal hyperplane classifier that can be easily retrained to peculiar writing styles. This combination allows for fast writer-dependent learning of new letters and symbols. The system is memory and speed efficient

Published in:

Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on

Date of Conference:

20-22 Oct 1993