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[Submitted on 17 Sep 2026] Title:Developing an OCR model for Extracting Information from Invoices with Korean Language View a PDF of the paper titled Developing an OCR model for Extracting Information from Invoices with Korean Language, by Xiem HoangVan and 3 other authors View PDF Abstract:Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Making the extraction of important information crucial. The stored information serves different purposes. Korean language is the native language of about 80 million people, playing an important role in not only South and North Korea but also in many other countries such as Vietnam, Philippine where a large number of Korean companies are located. In this context, to automatically extract proper information from the invoices with Korean language, we propose an efficient Optical Character Recognition (OCR) model in which a deep learning model is combined with some image preprocessing techniques. The proposed OCR model is assessed in a rich set of collected invoices showing that 87% F1-score can be achieved with negligible time processing. Comments: 2023 International Conference on Advanced Technologies for Communications (ATC) Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.35796 [cs.CL] (or arXiv:2609.35796v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.35796 arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1109/ATC58710.2023.10318877 DOI(s) linking to related resources Submission history From: Bao-Minh Dinh [view email] [v1] Thu, 17 Sep 2026 15:13:03 UTC (624 KB) Full-text links: Access Paper: View a PDF of the paper titled Developing an OCR model for Extracting Information from Invoices with Korean Language, by Xiem HoangVan and 3 other authors View PDF view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)