Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition

Authors:
Cheng-Lin Liu
Affiliations:
IEEE
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2007

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Abstract

The gradient direction histogram feature has shown superior performance in character recognition. To alleviate the effect of stroke direction distortion caused by shape normalization and provide higher recognition accuracies, we propose a new feature extraction approach, called normalization-cooperated gradient feature (NCGF) extraction, which maps the gradient direction elements of original image to direction planes without generating the normalized image and can be combined with various normalization methods. Experiments on handwritten Japanese and Chinese character databases show that, compared to normalization-based gradient feature, the NCGF reduces the recognition error rate by factors ranging from 8.63 percent to 14.97 percent with high confidence of significance when combined with pseudo-two-dimensional normalization.