A learning framework for degraded document image binarization using Markov Random Field

Document image binarization is an important preprocessing technique for document image analysis that segments the text from the document image backgrounds. Many techniques have been proposed and successfully applied in different applications, such as document image retrieval. However, these techniques may perform poorly on degraded document images. In this paper, we propose a learning framework that makes use of the Markov Random Field to improve the performance of the existing document image binarization methods for those degraded document images. Extensive experiments on the recent Document Image Bina-rization Contest datasets demonstrate that significant improvements of the existing binarization methods when applying our proposed framework.