Binarization algorithm for document image with complex background

Shaojun Miao; Tongwei Lu; Feng Min

doi:10.1117/12.2209016

14 December 2015 Binarization algorithm for document image with complex background

Shaojun Miao, Tongwei Lu, Feng Min

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981318 (2015) https://doi.org/10.1117/12.2209016
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

The most important step in image preprocessing for Optical Character Recognition (OCR) is binarization. Due to the complex background or varying light in the text image, binarization is a very difficult problem. This paper presents the improved binarization algorithm. The algorithm can be divided into several steps. First, the background approximation can be obtained by the polynomial fitting, and the text is sharpened by using bilateral filter. Second, the image contrast compensation is done to reduce the impact of light and improve contrast of the original image. Third, the first derivative of the pixels in the compensated image are calculated to get the average value of the threshold, then the edge detection is obtained. Fourth, the stroke width of the text is estimated through a measuring of distance between edge pixels. The final stroke width is determined by choosing the most frequent distance in the histogram. Fifth, according to the value of the final stroke width, the window size is calculated, then a local threshold estimation approach can begin to binaries the image. Finally, the small noise is removed based on the morphological operators. The experimental result shows that the proposed method can effectively remove the noise caused by complex background and varying light.

Citation Download Citation

Shaojun Miao, Tongwei Lu, and Feng Min "Binarization algorithm for document image with complex background", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981318 (14 December 2015); https://doi.org/10.1117/12.2209016

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