{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/docreal-robust-document-dewarping-of-real","title":"DocReal: Robust Document Dewarping of Real-Life Images via Attention-Enhanced Control Point Prediction","arxiv_id":null,"date":"2023-12-01","proceeding":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV-2024) 2023 12","authors":["Fangchen Yu","Yina Xie","Lei Wu","Yafei Wen","Guozhi Wang","Shuai Ren","Xiaoxin Chen","Jianfeng Mao","Wenye Li"],"abstract":"Document image dewarping is a crucial task in computer vision with numerous practical applications. The control point method, as a popular image dewarping approach, has attracted attention due to its simplicity and efficiency. However, inaccurate control point prediction due to varying background noises and deformation types can result in unsatisfactory performance. To address these issues, we propose a robust document dewarping approach for real-life images, namely DocReal, which utilizes Enet to effectively remove background noise and an attention-enhanced control point (AECP) module to better capture local deformations. Moreover, we augment the training data by synthesizing 2D images with 3D deformations and additional deformation types. Our proposed method achieves state-of-the-art performance on the DocUNet benchmark and a newly proposed benchmark of 200 Chinese distorted images, exhibiting superior dewarping accuracy, OCR performance, and robustness to various types of image distortion.","url_abs":"https://openaccess.thecvf.com/content/WACV2024/html/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.html","url_pdf":"https://openaccess.thecvf.com/content/WACV2024/papers/Yu_DocReal_Robust_Document_Dewarping_of_Real-Life_Images_via_Attention-Enhanced_Control_WACV_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"docreal-robust-document-dewarping-of-real","repo_url":"https://github.com/irisxcoding/docreal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"docreal-robust-document-dewarping-of-real","repo_url":"https://github.com/FaceOnLive/ID-Card-Passport-Recognition-SDK-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"docreal-robust-document-dewarping-of-real","repo_url":"https://github.com/SciYu/DocReal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"enet","method_name":"ENet"},{"method_slug":"enet-bottleneck","method_name":"ENet Bottleneck"},{"method_slug":"enet-dilated-bottleneck","method_name":"ENet Dilated Bottleneck"},{"method_slug":"enet-initial-block","method_name":"ENet Initial Block"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"prelu","method_name":"PReLU"},{"method_slug":"spatialdropout","method_name":"SpatialDropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}