On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Systems
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Robust Video Content Analysis via Transductive Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
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We recently designed a hand-printed text recognizer. The system is based on three set of experts respectively used to segment, classify and validate the text (with a French lexicon : 200K words). We present in this communication writer adaptation methods. The first is supervised by the user. The others are self-supervised strategies which compare classification hypothesis with lexical hypothesis and modify consequently classifier parameters. The last method increases the system accuracy and the classification speed. Experiments are presented on a large database of 90 texts (5400 words) written by 54 different writers and good recognition rates (82%) have been obtained.