A Robust Document Image Binarization Algorithm with texture features and fuzzy inference

• Main Authors: Wei-Shan, 趙偉善
• Other Authors: Chiun-Li Chin
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/65945572658466694219

碩士 === 中山醫學大學 === 應用資訊科學學系碩士班 === 99 === This paper proposes a new adaptive document image binarization algorithm for hand-held camera. This method can solve non-uniform illuminant problem. It is divided into two parts: the determination of block number of an image and the threshold value of block image. First, we will divide image into many equal-sized regions with texture features and artificial neural network. The Laws’ mask and sobel edge detector method are used to extract an image texture features. And then, these features are inputted into neural network. The learning algorithm of neural network uses the error back-propagation learning algorithm. Subsequently, the three features are extracted from each region. Finally, we use fuzzy inference method to determine the threshold value for each region. Tests on images produced under uniform and non-uniform illumination conditions show that our proposed method yields better visual quality and better OCR performance than three locally adaptive binarization methods.