Degraded historical document image binarization using local features and support vector machine (SVM)

This paper presents a support vector machine (SVM) based method for degraded historical document image binarization. Given a degraded historical document image, the proposed method first segments the image into w × w regions and implements a local contrast enhancement in each image block. We then use a SVM to select an optimal global threshold for binarization of each image block. Finally, the entire image is further binarized by a locally adaptive thresholding method. The proposed method has been evaluated over the recent Document Image Binarization Competition (DIBCO) datasets. The experimental results show that our proposed method outperforms other state-of-the-art techniques in terms of F-measure, NRM, DRD, and MPM.