Global Shape Normalization for Handwritten Chinese Character Recognition: A New Method

Authors:
Cheng-Lin Liu;Katsumi Marukawa
Affiliations:
Hitachi, Ltd.;Hitachi, Ltd.
Venue:
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Year:
2004

Citing 0
Cited 8

Building Compact Classifier for Large Character Set Recognition Using Discriminative Feature Extraction

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Binarization and cleanup of handwritten text from carbon copy medical form images

Pattern Recognition
Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition

Pattern Recognition
Handwritten Chinese character recognition: effects of shape normalization and feature extraction

SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach

SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

International Journal of Applied Mathematics and Computer Science
Extraction of handwritten text from carbon copy medical form images

DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Transcript mapping for handwritten Chinese documents by integrating character recognition model and geometric context

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.