Performance Evaluation of Binarization Methods for Document Images
Boran Şekeroğlu, Adnan Khashman · 2017
In scanned documents, where noise, contrast, and illumination vary, classifying pixels as foreground or background pixels is still a difficult and challenging problem. Several evaluation studies on binarization methods for document images were previously performed, however, performing an objective evaluation to determine an optimal binarization method is not trivial because of the application-dependency of the different methods and the varieties in document databases. In this paper, the aim is to determine an optimal binarization method that can be effectively used with a variety of scanned documents. Firstly, a comparative study of thirteen binarization methods applied to gray level images of degraded historical documents, artificially created words, and handwritten documents is presented. Secondly, three new image quality parameters, used for performance evaluation in addition to visual inspection of binarized images are proposed. Experimental results suggest that local method Water Flow Model and global methods Kapur and Otsu methods outperform the other ten binarization methods on all images.