Abstract

We present a genetic algorithm for online recognition of cursive handwriting. This recognition algorithm is designed so as to base its search on recognized locations in a handwritten word. In an attempt to retain important information, some statistical and lexical information are used to guide the mutation and crossover operators. Unlike more traditional analysis schemes, no scanning strategies have to be defined. The fitness function is designed to allow a self-organization of the recognized blocks.

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