Abstract
In this paper we propose a novel algorithm for optical character recognition in the presence of impulse noise by applying a wavelet transform, principal component analysis, and neural networks. In the proposed algorithm, the Haar wavelet transform is used for low frequency components allocation, noise elimination and feature extraction. The principal component analysis is used to reduce the dimension of the extracted features. We use a set of different multi-layer neural networks as classifiers for each character; the inputs are represented by a reduced set of features. One of the key features of the proposed approach is creating a separate neural network for each type of character. The experimental results show that the proposed algorithm can effectively recognize the characters in images in the presence of impulse noise; the results are comparable with ABBYY FineReader and Tesseract OCR.
| Original language | English |
|---|---|
| Pages (from-to) | 249-257 |
| Number of pages | 9 |
| Journal | Computer Optics |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Mar 2016 |
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Keywords
- Neural networks
- Optical character recognition
- Principal component analysis
- Wavelet transform
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering
- Computer Science Applications
Cite this
Using a haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise. / Spitsyn, Vladimir Grigorievich; Bolotova, Yuliya Alexandrovna; Phan, Ngoc Hoang; Bui, Thi Thu Trang.
In: Computer Optics, Vol. 40, No. 2, 01.03.2016, p. 249-257.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Using a haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise
AU - Spitsyn, Vladimir Grigorievich
AU - Bolotova, Yuliya Alexandrovna
AU - Phan, Ngoc Hoang
AU - Bui, Thi Thu Trang
PY - 2016/3/1
Y1 - 2016/3/1
N2 - In this paper we propose a novel algorithm for optical character recognition in the presence of impulse noise by applying a wavelet transform, principal component analysis, and neural networks. In the proposed algorithm, the Haar wavelet transform is used for low frequency components allocation, noise elimination and feature extraction. The principal component analysis is used to reduce the dimension of the extracted features. We use a set of different multi-layer neural networks as classifiers for each character; the inputs are represented by a reduced set of features. One of the key features of the proposed approach is creating a separate neural network for each type of character. The experimental results show that the proposed algorithm can effectively recognize the characters in images in the presence of impulse noise; the results are comparable with ABBYY FineReader and Tesseract OCR.
AB - In this paper we propose a novel algorithm for optical character recognition in the presence of impulse noise by applying a wavelet transform, principal component analysis, and neural networks. In the proposed algorithm, the Haar wavelet transform is used for low frequency components allocation, noise elimination and feature extraction. The principal component analysis is used to reduce the dimension of the extracted features. We use a set of different multi-layer neural networks as classifiers for each character; the inputs are represented by a reduced set of features. One of the key features of the proposed approach is creating a separate neural network for each type of character. The experimental results show that the proposed algorithm can effectively recognize the characters in images in the presence of impulse noise; the results are comparable with ABBYY FineReader and Tesseract OCR.
KW - Neural networks
KW - Optical character recognition
KW - Principal component analysis
KW - Wavelet transform
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U2 - 10.18287/2412-6179-2016-40-2-249-257
DO - 10.18287/2412-6179-2016-40-2-249-257
M3 - Article
VL - 40
SP - 249
EP - 257
JO - Computer Optics
JF - Computer Optics
SN - 0134-2452
IS - 2
ER -