Word-based Handwritten Arabic Scripts Recognition using DCT Features and Neural Network Classifier

被引：0
|
作者：
AlKhateeb, Jawad H. [1 ]
Ren, Jinchang [1 ]
Jiang, Jianmin [1 ]
Ipson, Stan S. [1 ]
El Abed, Haikal [2 ]
机构：
[1] Univ Bradford, Sch Informat EIMC, Bradford BD7 1DP, W Yorkshire, England
[2] Tech Univ Carolo Wilhelmina Braunschweig, Inst Commun Technol, D-38092 Braunschweig, Germany
来源：
2008 5TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS AND DEVICES, VOLS 1 AND 2 | 2008年
关键词：
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a system is proposed for word-based recognition of handwritten Arabic scripts. Techniques are discussed in details in term of three stages in the system, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, DCT features are extracted for each word sample. Finally, these features are then utilized to train a neural network for classification. The proposed system has been successfully tested on database (version v2.0ple) consisting of 32492 Arabic words handwritten by more than 1000 different writers, and the results were promising and very encouraging.
引用
收藏
页码：486 / +
页数：3
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