{"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/dan-a-segmentation-free-document-attention","title":"DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition","arxiv_id":"2203.12273","date":"2022-03-23","proceeding":null,"authors":["Denis Coquenet","Clément Chatelain","Thierry Paquet"],"abstract":"Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network. In addition to text recognition, the model is trained to label text parts using begin and end tags in an XML-like fashion. This model is made up of an FCN encoder for feature extraction and a stack of transformer decoder layers for a recurrent token-by-token prediction process. It takes whole text documents as input and sequentially outputs characters, as well as logical layout tokens. Contrary to the existing segmentation-based approaches, the model is trained without using any segmentation label. We achieve competitive results on the READ 2016 dataset at page level, as well as double-page level with a CER of 3.43% and 3.70%, respectively. We also provide results for the RIMES 2009 dataset at page level, reaching 4.54% of CER. We provide all source code and pre-trained model weights at https://github.com/FactoDeepLearning/DAN.","url_abs":"https://arxiv.org/abs/2203.12273v4","url_pdf":"https://arxiv.org/pdf/2203.12273v4.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":"dan-a-segmentation-free-document-attention","repo_url":"https://github.com/factodeeplearning/dan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"handwritten-document-recognition","task_name":"Handwritten Document Recognition"},{"task_slug":"handwritten-text-recognition","task_name":"Handwritten Text Recognition"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/handwritten-text-recognition-on-read-2016","task":"Handwritten Text Recognition","dataset":"READ 2016","model":"DAN","rank_in_archive_order":1,"of":2,"metrics":{"CER (%)":"3.22","WER (%)":"13.63"},"uses_additional_data":false},{"leaderboard":"/sota/handwritten-text-recognition-on-read2016-line","task":"Handwritten Text Recognition","dataset":"READ2016(line-level)","model":"DAN","rank_in_archive_order":3,"of":5,"metrics":{"Test CER":"4.1","Test WER":"17.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.12273","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}