Abstract
A new mathematical editor, based on the recognition of run-on discrete handwritten symbols, is proposed. The tested laboratory prototype of the system, modular and adaptable to the user habits and site requirements, uses a natural handwriting interface as well as human gestures. Two methods were used for symbol recognition, namely the state-of-the-art elastic matching algorithm and an Adaptive Resonance Theory neural architecture. The neural solution is proved to be better adapted to the cognitive nature of the problem and faster in both learning and test phases. Finally a novel attribute grammar permits the detection and subsequent correction of errors in the mathematical expressions.
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