MLPNN Based Handwritten Character Recognition Using Combined Feature Extraction

被引：0
|
作者：
Katiyar, Gauri [1 ]
Mehfuz, Shabana [1 ]
机构：
[1] Jamia Millia Islamia, Dept Elect Engn, New Delhi, India
来源：
2015 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION & AUTOMATION (ICCCA) | 2015年
关键词：
Handwritten character recognition; multilayer feed forward neural network; feature extraction; NEURAL-NETWORKS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Statistical techniques for off-line character recognition are not flexible and adaptive enough for new handwriting constraints. Offline handwritten character recognition of English alphabets using a three layered feed forward neural network is presented in this paper. The proposed recognition system describes the evaluation of feed forward neural network by combining four different feature extraction approaches(box approach, diagonal distance approach, mean and gradient operations). The proposed recognition system performs well on the benchmark dataset CEDAR (Centre of Excellence for Document Analysis And Recognition).
引用
收藏
页码：1155 / 1159
页数：5
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