FEATURE EXTRACTION OF HANDWRITTEN KANNADA CHARACTERS USING CURVELETS AND PRINCIPAL COMPONENT ANALYSIS

被引：0
|
作者：
Padma, M. C. [1 ]
Pasha, Saleem [2 ]
机构：
[1] PES Coll Engn, Dept Comp Sci & Engn, Mandya 571401, Karnataka, India
[2] PES Coll Engn, Dept Informat Sci & Engn, Mandya 571401, Karnataka, India
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2015年
关键词：
Optical character recognition; Curvelet transform; wrapping; principal component analysis; nearest neighbor classifier;
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：
Optical Character Recognition (OCR) is the well-known software product, which is used to automatically process the document images. It is defined as the process of converting scanned document images of machine printed or handwritten text into a computer editable format. In this paper, Wrapping based Curvelet transform is proposed to perform feature extraction. An attempt is also made to perform dimensionality reduction using principal component analysis. Nearest neighbor classifier is used to recognize the handwritten Kannada characters. The overall accuracy obtained using the proposed method is 90%.
引用
收藏
页码：1080 / 1084
页数：5
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