Spiral Recognition Methodology and Its Application for Recognition of Chinese Bank Checks

Abstract

This paper presents the spiral recognition methodology with its application in unconstrained handwritten Chinese legal amount recognition in a practical environment of a CheckReader/spl trade/. This paper first describes the failed application of neural network - hidden Markov model hybrid recognizer on Chinese bank check legal amount recognition, and explains the reasons for the failure: the neural network - hidden Markov model hybrid recognizer cannot handle the complexity in the training for Chinese legal amounts. Then a spiral recognition methodology is presented. This methodology enables the system to increase its recognition power (both the recognition rate and the number of recognized characters) during the training iterations. Some experiments were done to show that the spiral recognition methodology has a high performance in the recognition of unconstrained handwritten Chinese legal amounts. The recognition rate at the character level is 93.5%, and the recognition rate at the legal amount level is 60%. Combined with the recognition of courtesy amount, the overall error rate is less than 1%.