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Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISBN: 0-7695-2187-8
pp: 300-305
Cheng-Lin Liu , Hitachi, Ltd.
Katsumi Marukawa , Hitachi, Ltd.
Nonlinear normalization (NLN) based on line density equalization has been widely used in handwritten Chinese character recognition (HCCR). Our previous results showed that global transformation methods, including moment normalization and a newly proposed bi-moment method, generate smooth normalized shapes at lower computation effort while yielding comparable recognition accuracies. This paper proposes a new global transformation method, named modified centroid-boundary alignment (MCBA) method, for HCCR. The previous CBA method can efficiently correct the skewness of centroid by quadratic curve fitting but fails to adjust the inner density. The MCBA method adds a simple trigonometric (sine) function onto quadratic function to adjust the inner density. The amplitude of the sine wave is estimated from the centroids of half images. Experiments on the ETL9B and JEITA-HP databases show that the MCBA method yields comparably high accuracies to the NLN and bi-moment methods and shows complementariness.
Cheng-Lin Liu, Katsumi Marukawa, "Global Shape Normalization for Handwritten Chinese Character Recognition: A New Method", IWFHR, 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition 2004, pp. 300-305, doi:10.1109/IWFHR.2004.47
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