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User adaptive handwriting recognition by self-growing probabilistic decision-based neural networks | IEEE Journals & Magazine | IEEE Xplore

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


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-r...Show More

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.
Published in: IEEE Transactions on Neural Networks ( Volume: 11, Issue: 6, November 2000)
Page(s): 1373 - 1384
Date of Publication: 30 November 2000

ISSN Information:

PubMed ID: 18249861

References

References is not available for this document.