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Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISBN: 0-7695-2187-8
pp: 263-268
Hanshen Tang , Concordia University
Emmanuel Augustin , Artificial Intelligence and Image Analysis
Ching Y. Suen , Concordia University
Olivier Baret , Artificial Intelligence and Image Analysis
Mohamed Cheriet , ?cole de Technologie Sup?rieure
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™. 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 can not 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%.
INDEX TERMS
spiral recognition methodology, offline handwriting recognition, Asian character recognition
CITATION
Hanshen Tang, Emmanuel Augustin, Ching Y. Suen, Olivier Baret, Mohamed Cheriet, "Spiral Recognition Methodology and Its Application for Recognition of Chinese Bank Checks", IWFHR, 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition 2004, pp. 263-268, doi:10.1109/IWFHR.2004.96
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