Context-Aware Systems and Applications. 4th International Conference, ICCASA 2015, Vung Tau, Vietnam, November 26-27, 2015, Revised Selected Papers

Research Article

Context-Aware Handwritten and Optical Character Recognition Using a Combination of Wavelet Transform, PCA and Neural Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-29236-6_25,
        author={Ngoc Phan and Thi Bui},
        title={Context-Aware Handwritten and Optical Character Recognition Using a Combination of Wavelet Transform, PCA and Neural Networks},
        proceedings={Context-Aware Systems and Applications. 4th International Conference, ICCASA 2015, Vung Tau, Vietnam, November 26-27, 2015, Revised Selected Papers},
        proceedings_a={ICCASA},
        year={2016},
        month={4},
        keywords={Character recognition Wavelet transform PCA Neural network Image processing},
        doi={10.1007/978-3-319-29236-6_25}
    }
    
  • Ngoc Phan
    Thi Bui
    Year: 2016
    Context-Aware Handwritten and Optical Character Recognition Using a Combination of Wavelet Transform, PCA and Neural Networks
    ICCASA
    Springer
    DOI: 10.1007/978-3-319-29236-6_25
Ngoc Phan1,*, Thi Bui1,*
  • 1: Ba Ria-Vung Tau University
*Contact email: hoangpn285@gmail.com, trangbt.084@gmail.com

Abstract

This paper proposes a novel context-aware handwritten and optical character recognition algorithm using a combination of wavelet transform, PCA and neural networks. At first, the features of character are extracted using combination of wavelet transform and PCA. Then multi-layer feed-forward neural networks will be used to classify these extracted features. In this algorithm, we use one neural network for each training character. This neural network is used to determine whether an input character is training character or not. The paper experimental results show that the proposed algorithm gives an effective performance of character recognition on noisy images and competes with state-of-the-art algorithms.