2020/07/14 by Weihong Ma, Ma, Weihong, Hesuo Zhang +9 · 5 citations
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Image Processing and 3D Reconstruction #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.2007.06890
6 pages, 6 figures
arxiv created 2020/07/14 · openalex publication_date 2020/07/14 · arxiv updated 2020/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine character 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 Experiment results on the extended Chinese historical document MTHv2 dataset demonstrate the effectiveness of the proposed framework.