Evolutionary computation for the recognition of on-line cursive handwriting

This paper describes a system that recognizes on-line cursive handwriting. The system was specialized on Arabic script, but it may be adapted to work on any other language. A genetic algorithm is used to select the best combination of characters recognized by the hierarchical beta neuro-fuzzy system. The handwritten words are modeled by a neuro-physiological theory of movement generation predicting that the main features extracted from each character are the parameters of the equation describing the curvilinear velocity of the script. The evolutionary approach proposed here permits the recognition of cursive handwriting with a segmentation procedure allowing overlapped strokes having neuro-physiological meaning. We also present the experimental results obtained when using the system to recognize on-line handwritten Arabic.