Deductive method for recognition of on-line handwritten Persian/Arabic characters

The choice of relevant techniques in preprocessing, segmentation and feature extraction is very efficient and effective in rate of online handwriting recognition system. This paper presents a novel deductive method for detecting critical points of the Persian/Arabic handwritten character system in all their different shapes. The implemented method has increased the performance rate of the online Persian/Arabic handwritten recognition system and has decreased the computational mistake for finding critical points. This method helps us to extract stroke of each online handwritten letter and then divided each stroke into some parts, i.e. tokens. The minimal features set are collected from these tokens and encoding to a classifier. The neural network classifier is designed with a robust weight initialization method. Finally, a database set of the Persian handwritten character samples has been employed to test the system in all their different shapes.