{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T08:12:00Z","timestamp":1750407120343,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>The adoption of Optical Character Recognition (OCR) tools has been central to the increased digitization of historical documents. However, the errors introduced during OCR, particularly in texts with a specialized vocabulary (SV), necessitate effective post-OCR correction methodologies. This study introduces a novel approach that leverages weak supervision and self-supervised fine-tuning to enhance post-OCR correction without the need for substantial manual annotations. By using multi-noise-level synthetic data, generated through automatically-extracted OCR errors and applied to clean texts, we can train robust models tailored for post-OCR tasks. Furthermore, we propose a unique self-supervised fine-tuning strategy, applied specifically to long texts, enables models to adeptly handle out-of-vocabulary problems and SV. Additionally, we tested the performance of the GPT model on post-OCR tasks.<\/jats:p>","DOI":"10.3233\/faia240577","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:52:08Z","timestamp":1729169528000},"source":"Crossref","is-referenced-by-count":1,"title":["Synthetically Augmented Self-Supervised Fine-Tuning for Diverse Text OCR Correction"],"prefix":"10.3233","author":[{"given":"Shuhao","family":"Guan","sequence":"first","affiliation":[{"name":"Insight Centre for Data Analytics, Dublin School of Computer Science, University College Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Derek","family":"Greene","sequence":"additional","affiliation":[{"name":"Insight Centre for Data Analytics, Dublin School of Computer Science, University College Dublin, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240577","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:52:08Z","timestamp":1729169528000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240577"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240577","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}