Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (33): 172-174.DOI: 10.3778/j.issn.1002-8331.2010.33.048

• 图形、图像、模式识别 • Previous Articles     Next Articles

Handwritten digits recognition using HMM based on contour feature

XIAO Ming,JIA Zhen-hong   

  1. School of Information Science and Engineering,Xinjiang University,Urumqi 830046,China
  • Received:2009-03-31 Revised:2009-05-20 Online:2010-11-21 Published:2010-11-21
  • Contact: XIAO Ming

基于轮廓特征的HMM手写数字识别

肖 明,贾振红   

  1. 新疆大学 信息科学与工程学院,乌鲁木齐 830046
  • 通讯作者: 肖 明

Abstract: Boundary chain code and the ring constitute a complete description of character contours in the handwritten digits recognition.24 kinds of strokes are constructed based on the characteristic of handwritten digits.Firstly the boundary chain code is transformed to a component feature by 24 strokes and together with the ring feature,which constitute the entire feature.Then the feature is recognized by using Hidden Markov Model(HMM).The contour feature which is applied in the handwritten digits recognition of HMM is introduced firstly.The recognition rate of 92.2% is obtained from the recognition of MNIST database.

Key words: Hidden Markov Model(HMM), digits recognition, boundary chain code, stroke

摘要: 在手写数字识别中,边界链码和环构成了对字符轮廓的完整描述。针对手写数字的特点,建了24种笔划。首先将样本边界链码转化成由24个笔划组成的特征值,再加上环特征,构成整个特征值。然后利用隐马尔可夫模型(HMM)对提取的特征值进行分类识别。首次将字符轮廓特征应用在基于HMM的手写数字识别中,在识别MNIST字库上,取得了92.2%的识别率。

关键词: 隐马尔可夫模型(HMM), 数字识别, 边界链码, 笔划

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