Writer Style Adaptation in Online Handwriting Recognizers by a Fuzzy Mechanism Approach: the Adapt Method

This study presents an automatic on-line adaptation mechanism to the handwriting style of a writer for the recognition of isolated handwritten characters. The classifier we use here is based on a Fuzzy Inference System (FIS) similar to those we have designed for handwriting recognition. In this FIS each premise rule is composed of a fuzzy prototype which represents intrinsic properties of a class. Furthermore, the conclusion part of rules associates a score to the prototype for each class. The adaptation mechanism affects both the conclusions of the rules and the fuzzy prototypes by re-centering and re-shaping them thanks to a new approach called ADAPT inspired by the Learning Vector Quantization. Thus the FIS is automatically fitted to the handwriting style of the writer that currently uses the system. Our adaptation mechanism is compared with well known adaptation techniques. The tests were based on eight different writers and the results illustrate the benefits of the method in term of error rate reduction (86% in average). This allows such kind of simple classifiers to achieve up to 98.4% of recognition accuracy on the 26 Latin letters in a writer dependent context.