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In this paper, we propose an end-to-end trainable framework for restoringhistorical documents content that follows the correct reading order. In thisframework, two branches named character branch and layout branch are addedbehind the feature extraction network. The character branch localizesindividual characters in a document image and recognizes them simultaneously.Then we adopt a post-processing method to group them into text lines. Thelayout branch based on fully convolutional network outputs a binary mask. Wethen use Hough transform for line detection on the binary mask and combinecharacter results with the layout information to restore document content.These two branches can be trained in parallel and are easy to train.Furthermore, we propose a re-score mechanism to minimize recognition error.Experiment results on the extended Chinese historical document MTHv2 datasetdemonstrate the effectiveness of the proposed framework.

Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization