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A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer
Joseph J LaViola1, Robert C Zeleznik
1School of Electrical Engineering and Computer Science, University of Central Florida, Engineering 3--Harris Center, Orlando, FL 32816-2362, USA. jjl@cs.ucf.edu
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 13, 2007
Summary
This study introduces a novel method combining writer-independent and writer-dependent handwriting recognition. This approach enhances accuracy and speed while minimizing training needs for symbol recognition systems.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Writer-dependent symbol recognizers offer high accuracy but require extensive user-specific training.
- Writer-independent recognizers have broader applicability but often lack personalization for optimal performance.
- Integrating both approaches presents a challenge in balancing accuracy, speed, and training efficiency.
Purpose of the Study:
- To develop a practical technique that leverages a writer-independent recognition engine to enhance a writer-dependent symbol recognizer.
- To improve the accuracy and speed of symbol recognition while significantly reducing user training requirements.
- To explore the synergistic benefits of combining different recognition paradigms for handwriting analysis.
Main Methods:
- Utilized a writer-dependent recognizer employing AdaBoost for pairwise symbol comparisons.
- Integrated a writer-independent handwriting recognizer as a weak learner within the AdaBoost classifiers.
- Implemented an n-best list from the writer-independent recognizer to prune candidate symbols during online recognition, reducing computational load.
- Described the geometric and statistical features and the all-pairs classification algorithm used.
Main Results:
- Demonstrated significant improvements in accuracy and recognition speed through the integrated approach.
- Quantified a substantial reduction in the user training time needed for the writer-dependent system.
- Showcased the effectiveness of using writer-independent recognition to guide writer-dependent classification.
Conclusions:
- The proposed technique effectively combines writer-independent and writer-dependent recognition engines for superior symbol recognition performance.
- This hybrid approach offers a practical solution for developing accurate, fast, and user-friendly handwriting recognition systems.
- The findings suggest a promising direction for reducing the practical barriers associated with personalized handwriting recognition.
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