Design of a neural network character recognizer for a touch terminal
Pattern Recognition
Integrated segmentation and recognition of hand-printed numerals
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
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This paper addresses the problem of the automatic recognition of handwritten text. The text to be recognized is captured on-line and the temporal sequence of the data is preserved. The approach is based on a left to right hidden Markov model for each character that models the dynamics of the written script. A mixture of Gaussian distributions is used to represent the output probabilities at each arc of the HMM. Several strategies for reestimating the model parameters are discussed. Experiments show that this approach results in significant decreases in error rate for the recognition of discretely written characters compared to elastic matching techniques.