Evolution of Multiple States Machines for Recognition of Online Cursive Handwriting

Recognition of cursive handwritings such as Persian script is a hard task as there is no fixed segmentation and simultaneous segmentation and recognition is required. This paper presents a novel comparison method for such tasks which is based on a Multiple States Machine to perform robust elastic comparison of small segments with high speed through generation and maintenance of a set of concurrent possible hypotheses. The approach is implemented on Persian (Farsi) language using a typical feature set and a specific tailored genetic algorithm and the recognition and computation time is compared with dynamic programming comparison approach.