Page Segmentation for Historical Handwritten Documents Using Fully Convolutional Networks

Page segmentation is a fundamental and challenging task in document image analysis due to the layout diversity. In this work, we propose a pixel-wise segmentation method for historical handwritten documents using fully convolutional network (FCN). The document image is segmented into different regions by classifying pixels into different categories: background, main text body, comments, and decorations. By supervised learning on document images with pixel-wise labels, the FCN can extract discriminative features and perform pixel-wise segmentation accurately. After pixel-wise classification, post-processing steps are taken to reduce noises, correct wrong segmentations and find out overlapping regions. Experimental results on the public dataset DIVA-HisDB containing challenging medieval manuscripts demonstrate the effectiveness and superiority of the proposed method, which yields pixel-level accuracy of above 99%.