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Normalization-cooperated gradient feature extraction for handwritten character recognition
Liu, Cheng-Lin
2007-08-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷号29期号:8页码:1465-1469
文章类型Article
摘要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.
关键词Character Recognition Feature Extraction Normalization-cooperated Gradient Feature (Ncgf)
WOS标题词Science & Technology ; Technology
关键词[WOS]DIGIT RECOGNITION
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000247186500013
引用统计
被引频次:65[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9507
专题09年以前成果
作者单位Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100080, Peoples R China
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GB/T 7714
Liu, Cheng-Lin. Normalization-cooperated gradient feature extraction for handwritten character recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2007,29(8):1465-1469.
APA Liu, Cheng-Lin.(2007).Normalization-cooperated gradient feature extraction for handwritten character recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,29(8),1465-1469.
MLA Liu, Cheng-Lin."Normalization-cooperated gradient feature extraction for handwritten character recognition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 29.8(2007):1465-1469.
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