Offline Handwritten Numeral Recognition Based on Principal Component Analysis

摘要：

To overcome the difficulty of fusing statistical feature and structural feature in the research on handwritten numeral recognition, Principal Component Analysis is used to reconstruct numeral model and estimate the numeral reconstructive error based on the statistical information of digit structural feature. At the same time, the height-width ratio and Euler value of numeral is extracted. Recognition of the digit character is completed through combining the neural network and Bayes classifier respectively corresponding to the three type features. The recognition rate of this method is 90.73％ on handwritten numeral database.

关键词： Handwritten Numeral Recognition Principal Component Feature Extracting Combining Classifiers

作者: Wan Junli Huang Yuehua Zhang Guohua Wan Cheng

作者单位: China Three Gorges University,Yichang 443002 China Wuhan University,Wuhan 430072 China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）