An Effective Binarization Method for Disturbed Camera-Captured Document Images
Jinyuan Zhao, Cunzhao Shi, Fuxi Jia, Yanna Wang, Baihua Xiao · 2018
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.