Recognition of strings using nonstationary Markovian models: an application in ZIP code recognition

This paper presents nonstationary Markovian models and their application to recognition of strings of tokens, such as ZIP codes in the US mailstream. Unlike traditional approaches where digits are simply recognized in isolation, the novelty of our approach lies in the manner in which recognitions scores along with domain specific knowledge about the frequency distribution of various combination of digits are all integrated into one unified model. The domain knowledge is derived from postal directory files. This data feeds into the models as n-grams statistics that are seamlessly integrated with recognition scores of digit images. We present the recognition accuracy (90%) achieved on a set of 20,000 ZIP codes.