A feature extraction technique for online handwriting recognition

被引：0
|
作者：
Verma, B [1 ]
Lu, J [1 ]
Ghosh, M [1 ]
Ghosh, R [1 ]
机构：
[1] Univ Cent Queensland, Fac Informat & Commun, Rockhampton, Qld 4702, Australia
来源：
2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年
关键词：
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：
The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a Neural Network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.
引用
收藏
页码：1337 / 1341
页数：5
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