User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks

Authors:
Hsin-Chia Fu;Hung-Yuan Chang;Yeong Yuh Xu;H. -T. Pao
Affiliations:
Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2000

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Abstract

Based on self-growing probabilistic decision-based neural networks (SPDNNs), user adaptation of the parameters of SPDNN is formulated as incremental reinforced and anti-reinforced learning procedures, which are easily integrated into the batched training procedures of the SPDNN. In this study, we developed: 1) an SPDNN based handwriting recognition system; 2) a two-stage recognition structure; and 3) a three-phase training methodology for a global coarse classifier (stage 1), a user independent hand written character recognizer (stage 2), and a user adaptation module on a personal computer. With training and testing on a 600-word commonly used Chinese character set, the recognition results indicate that the user adaptation module significantly improved the recognition accuracy. The average recognition rate increased from 44.2% to 82.4% in five adapting cycles, and the performance could finally increase up to 90.2% in ten adapting cycles.