TOWARDS AN ART BASED MATHEMATICAL EDITOR, THAT USES ONLINE HANDWRITTEN SYMBOL RECOGNITION

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 requirem ents, 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 t he cognitive nature of the problem and faster in both learning and tes t phases. Finally a novel attribute grammar permits the detection and subsequent correction of errors in the mathematical expressions.