{"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/dpcspell-a-transformer-based-detector","title":"DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages","arxiv_id":"2211.03730","date":"2022-11-07","proceeding":null,"authors":["Mehedi Hasan Bijoy","Nahid Hossain","Salekul Islam","Swakkhar Shatabda"],"abstract":"Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.","url_abs":"https://arxiv.org/abs/2211.03730v3","url_pdf":"https://arxiv.org/pdf/2211.03730v3.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":"dpcspell-a-transformer-based-detector","repo_url":"https://github.com/mehedihasanbijoy/DPCSpell","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bangla-spelling-error-correction","task_name":"Bangla Spelling Error Correction"},{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[{"slug":"dpcspell-bangla-sec-corpus","name":"DPCSpell-Bangla-SEC-Corpus","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/bangla-spelling-error-correction-on-dpcspell","task":"Bangla Spelling Error Correction","dataset":"DPCSpell-Bangla-SEC-Corpus","model":"DPCSpell","rank_in_archive_order":1,"of":4,"metrics":{"Exact Match Accuracy":"94.78"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}