Efficient Document Image Binarization
Kiju Lee · TECHART Journal of Arts and Imaging Science · 2014
In this paper, we present an efficient and adaptive method for document image binarization using degraded images. Our contributions lead to improvements in both the binarization quality and speed. The vertical and horizontal strokes of Korean and Chinese characters are detected adaptively using a local means for accurate binarization. A down-sampled integral image is employed to quickly compute the local means. The proposed method can deal with various degradations owing to shadows, non-uniform illumination, low contrast, and high levels of signal-dependent noise. The experimental results show that the proposed method is about 11- to 424-times faster than existing algorithms and provides better quality results. Therefore, the proposed efficient document binarization algorithm can be a useful tool in fast documentation and optical character recognition.