The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform

Baihua Xiao - One of the best experts on this subject based on the ideXlab platform.

  • ICFHR - An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images

  • An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images.

Jinyuan Zhao - One of the best experts on this subject based on the ideXlab platform.

  • ICFHR - An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images

  • An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images.

Yanna Wang - One of the best experts on this subject based on the ideXlab platform.

  • ICFHR - An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images

  • An Effective Binarization Method for Disturbed Camera-Captured Document Images
    2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 2018
    Co-Authors: Jinyuan Zhao, Yanna Wang, Baihua Xiao
    Abstract:

    Many researchers make numerous work on document image binarization. However, the binarization results of camera-captured document images remain to be improved due to many disturbances such as creases, noises and shadows. To binarize these images effectively, this paper proposes an adaptive local thresholding method which takes advantages of multi-level multi-scale local statistical information. By using the context information of multiple scales, the pixels in the image are classified by coarse to fine. The majority of background areas were removed by multiscale analysis of variance. For the text area, the binarization threshold is dynamically adjusted according to the estimated clarity. Our method can make the grayscale image binarization directly, without adding any Postprocessing Operation. The experimental results show that our method can significantly improve the performance of OCR system and is also suitable for degraded document images.

M. Stevanovic - One of the best experts on this subject based on the ideXlab platform.

P. Petrovic - One of the best experts on this subject based on the ideXlab platform.