A system for cursive handwritten address recognition
نویسندگان
چکیده
This paper presents a cursive handwritten address recognition system which con sists of four main modules i over segmentor ii dynamic zip locator iii zip can didates generator and iv city state zip veri er The dynamic zip locator and city state zip veri er are based on a exible matcher for matching a sequence of graphemes with a list of generalized strings The dynamic zip locator is able to locate zip with out knowing exactly where the zip starts The zip candidates generator uses a hidden Markov model HMM with position dependent state transition probabilities A re duced dynamic programming table and a scheme for utilizing pre xes are designed to reduce computation and memory requirement Finally the system employs a mech anism for rejection based on rank features extracted from the matching The overall system achieves an acceptance rate of with error for digit encoding on USPS cursive address images
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