Using a Haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise
| Autor: | V. G. Spitsyn, T. T. T. Bui, Yu. A. Bolotova, NhatHai Phan |
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| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Computer science business.industry 010102 general mathematics Feature extraction 0211 other engineering and technologies Wavelet transform Pattern recognition 02 engineering and technology Optical character recognition computer.software_genre Impulse noise 01 natural sciences Atomic and Molecular Physics and Optics Haar wavelet Computer Science Applications 021105 building & construction Principal component analysis Tesseract Artificial intelligence 0101 mathematics Electrical and Electronic Engineering business computer |
| Zdroj: | Computer Optics. 40:249-257 |
| ISSN: | 2412-6179 0134-2452 |
| DOI: | 10.18287/2412-6179-2016-40-2-249-257 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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