Handwritten Numeral String Recognition: Character-Level vs. String-Level Classifier Training

Authors:
Cheng-Lin Liu;Katsumi Marukawa
Affiliations:
Hitachi, Ltd., Japan;Hitachi, Ltd., Japan
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Abstract

The performance of handwritten numeral string recognition integrating segmentation and classification relies on the classification accuracy and the resistance to non-characters of the underlying classifier. The classifier can be trained at either character level (with character and non-character samples) or string level (with string samples). We show that both character-level and string-level training yield superior string recognition performance. String-level training improves segmentation but deteriorates classification. By combining the character-level trained classifier and the string-level trained classifier, we have achieved higher string recognition performance. We show the experimental results of three classifier structures on the numeral strings of NIST Special Database 19.