Features Extraction for Offline Handwritten Character Recognition

被引：1
|
作者：
Benchaou, Soukaina [1 ]
Nasri, M'barek [1 ]
El Melhaoui, Ouafae [1 ]
机构：
[1] Univ Mohammed 1, EST, Lab MATSI, Oujda, Morocco
来源：
EUROPE AND MENA COOPERATION ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGIES | 2017年 / 520卷
关键词：
Offline handwritten character; Feature extraction; K nearest neighbors; Fuzzy min max;
D O I：
10.1007/978-3-319-46568-5_21
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Offline handwritten character recognition has been one of the most challenging research areas in the field of image processing and pattern recognition in the recent years. Handwritten character recognition is a very problematic research area because writing styles may vary from one user to another. This paper throws light on four different feature techniques, Zoning, Profile projection, Freeman chain code and Histograms of oriented gradients for handwritten vowels recognition. The recognition is carried out in this work through K nearest neighbors and fuzzy min max classification methods. The best recognition rate of 96 % was obtained using Histogram of oriented gradients features.
引用
收藏
页码：209 / 217
页数：9
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