A system for cursive handwritten address recognition

This paper presents a cursive handwritten address recognition system, which consists of four main modules; 1) over-segmentor, 2) dynamic zip locator, 3) zip candidates generator, and 4) city-state-zip verifier. The dynamic zip locator and city-state-zip verifier are based on a flexible matcher for matching a sequence of graphemes with a list of generalized strings. The dynamic zip locator is able to locate zip without knowing exactly where the zip starts. The zip candidates generator uses a hidden Markov model with position-dependent state transition probabilities. A scheme for utilizing prefixes is designed to reduce computation and memory requirement. Finally, the system employs a mechanism for rejection based on rank features extracted from the matching. The overall system achieves an acceptance rate of 83.5% with 3.6% error for 5-digit encoding on 805 USPS cursive address images.