{"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/upcycle-your-ocr-reusing-ocrs-for-post-ocr","title":"Upcycle Your OCR: Reusing OCRs for Post-OCR Text Correction in Romanised Sanskrit","arxiv_id":"1809.02147","date":"2018-09-06","proceeding":"CONLL 2018 10","authors":["Amrith Krishna","Bodhisattwa Prasad Majumder","Rajesh Shreedhar Bhat","Pawan Goyal"],"abstract":"We propose a post-OCR text correction approach for digitising texts in\nRomanised Sanskrit. Owing to the lack of resources our approach uses OCR models\ntrained for other languages written in Roman. Currently, there exists no\ndataset available for Romanised Sanskrit OCR. So, we bootstrap a dataset of 430\nimages, scanned in two different settings and their corresponding ground truth.\nFor training, we synthetically generate training images for both the settings.\nWe find that the use of copying mechanism (Gu et al., 2016) yields a percentage\nincrease of 7.69 in Character Recognition Rate (CRR) than the current state of\nthe art model in solving monotone sequence-to-sequence tasks (Schnober et al.,\n2016). We find that our system is robust in combating OCR-prone errors, as it\nobtains a CRR of 87.01% from an OCR output with CRR of 35.76% for one of the\ndataset settings. A human judgment survey performed on the models shows that\nour proposed model results in predictions which are faster to comprehend and\nfaster to improve for a human than the other systems.","url_abs":"http://arxiv.org/abs/1809.02147v1","url_pdf":"http://arxiv.org/pdf/1809.02147v1.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":"upcycle-your-ocr-reusing-ocrs-for-post-ocr","repo_url":"https://github.com/majumderb/sanskrit-ocr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.02147","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}