Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition

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-coopera...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 29; no. 8; pp. 1465 - 1469
Main Author Liu, Cheng-Lin
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.08.2007
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Applied sciences
Artificial intelligence
Character recognition
Computer science; control theory; systems
Error analysis
Exact sciences and technology
Extraction
Feature extraction
Feature recognition
Handwriting recognition
Histograms
Image databases
Image generation
Image recognition
Intelligence
normalization-cooperated gradient feature (NCGF)
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
Recognition
Shape
Spatial databases
Gradient
Histogram
Image processing
normalization- cooperated gradient feature (NCGF)
Pattern recognition
Character recognition
Japanese
Confidence
Database
Chinese
Feature extraction
Manuscript character
Pattern analysis
Artificial intelligence
Pattern extraction
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Summary: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.
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2007.1090