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

H C Fu¹, H Y Chang, Y Y Xu

¹Department of Computer Science and Information Engineering, National Chiao Tung University, Hsin-Chu, Taiwan, R.O.C.

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

User adaptation significantly improves handwriting recognition accuracy, especially with limited training data. Self-growing probabilistic decision-based neural networks (SPDNNs) enable efficient model initialization for new users, boosting performance.

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Area of Science:

Artificial Intelligence
Machine Learning
Pattern Recognition

Background:

User-dependent handwriting recognition systems typically outperform user-independent ones with sufficient data.
Limited user-specific training data hinders the performance of user-dependent systems.
Leveraging existing knowledge from multi-user databases can improve performance with minimal training data.

Purpose of the Study:

To address user adaptation challenges in handwriting recognition systems.
To develop an effective user adaptation module using self-growing probabilistic decision-based neural networks (SPDNNs).
To enhance recognition accuracy when user-specific training data is scarce.

Main Methods:

Developed an SPDNN-based handwriting recognition system.

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Implemented a two-stage recognition structure with a global coarse classifier and a user-independent recognizer.

Utilized a three-phase training methodology incorporating incremental reinforced and anti-reinforced learning for user adaptation.

Main Results:

The user adaptation module significantly improved recognition accuracy on a 600-word Chinese character set.
Average recognition rate increased from 44.2% to 82.4% within five adaptation cycles.
Recognition performance reached up to 90.2% after ten adaptation cycles.

Conclusions:

The proposed SPDNN-based user adaptation method effectively enhances handwriting recognition accuracy.
This approach is particularly beneficial for scenarios with limited user-specific training data.
The developed system demonstrates a practical solution for personalized handwriting recognition.