{"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/improving-ocr-accuracy-on-early-printed-books-2","title":"Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting","arxiv_id":"1711.09670","date":"2017-11-27","proceeding":null,"authors":["Christian Reul","Uwe Springmann","Christoph Wick","Frank Puppe"],"abstract":"In this paper we introduce a method that significantly reduces the character\nerror rates for OCR text obtained from OCRopus models trained on early printed\nbooks. The method uses a combination of cross fold training and confidence\nbased voting. After allocating the available ground truth in different subsets\nseveral training processes are performed, each resulting in a specific OCR\nmodel. The OCR text generated by these models then gets voted to determine the\nfinal output by taking the recognized characters, their alternatives, and the\nconfidence values assigned to each character into consideration. Experiments on\nseven early printed books show that the proposed method outperforms the\nstandard approach considerably by reducing the amount of errors by up to 50%\nand more.","url_abs":"http://arxiv.org/abs/1711.09670v1","url_pdf":"http://arxiv.org/pdf/1711.09670v1.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":"improving-ocr-accuracy-on-early-printed-books-2","repo_url":"https://github.com/chreul/mptv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}