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Spiral recognition methodology and its application for recognition of Chinese bank checks

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5 Author(s)
Hanshen Tang ; Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada ; E. Augustin ; C. Y. Suen ; O. Baret
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This paper presents the spiral recognition methodology with its application in unconstrained handwritten Chinese legal amount recognition in a practical environment of a CheckReader™. 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%.

Published in:

Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on

Date of Conference:

26-29 Oct. 2004