Using a haar wavelet transform, principal component analysis and neural networks for OCR in the presence of impulse noise

Vladimir Grigorievich Spitsyn, Yuliya Alexandrovna Bolotova, Ngoc Hoang Phan, Thi Thu Trang Bui

Research output: Contribution to journalArticle

22 Citations (Scopus)


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 languageEnglish
Pages (from-to)249-257
Number of pages9
JournalComputer Optics
Issue number2
Publication statusPublished - 1 Mar 2016



  • 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

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