Off-Line Handwritten Odia Character Recognition Using DWT and PCA

被引：7

作者：

Sethy, Abhisek ^{[1
]}

Patra, Prashanta Kumar ^{[1
]}

Nayak, Deepak Ranjan ^{[2
]}

机构：

[1] Coll Engn & Technol, Dept Comp Sci & Engn, Bhubaneswar, Odisha, India

[2] Natl Inst Technol, Dept Comp Sci & Engn, Rourkela, India

来源：

PROGRESS IN ADVANCED COMPUTING AND INTELLIGENT ENGINEERING, PROCEEDINGS OF ICACIE 2016, VOLUME 1 | 2018年 / 563卷

关键词：

Discrete wavelet transform (DWT); Optical character recognition (OCR); Principal component analysis (PCA);

D O I：

10.1007/978-981-10-6872-0_18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a new approach for Odia handwritten character recognition based on discrete wavelet transform (DWT) and principal component analysis (PCA). Statistical feature descriptors like mean, standard deviation, energy have been computed from each sub-band of the second level DWT and are served as the primary features. To find the most significant features, PCA is applied. Subsequently, back-propagation neural network (BPNN) is harnessed to perform the classification task. The proposed method is validated on a standard Odia dataset, containing 150 samples from each of the 47 categories. The simulation results offer a recognition rate of 94.8%.

引用

页码：187 / 195

页数：9

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