Title :
Writer-adaptation for on-line handwritten character recognition
Author :
Matic, N. ; Guyon, I. ; Denker, J. ; Vapnik, V.
Author_Institution :
AT&T Bell Lab., Holmdel, NJ, USA
Abstract :
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
Keywords :
handwriting recognition; image classification; neural nets; optical character recognition; TDNN; Time Delay Neural Network; on-line handwritten character recognition; online characters; optimal hyperplane classifier; peculiar writing styles; touch terminal; uppercase letters; writer adaptation; writer-adaptable character recognition system; writer-dependent learning; Application software; Backpropagation algorithms; Character recognition; Degradation; Delay effects; Feature extraction; Handwriting recognition; Neural networks; Speech; Writing;
Conference_Titel :
Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on
Conference_Location :
Tsukuba Science City
Print_ISBN :
0-8186-4960-7
DOI :
10.1109/ICDAR.1993.395752
