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Developing an OCR model for Extracting Information from Invoices with Korean Language

A team of researchers has developed an Optical Character Recognition (OCR) model that combines a deep learning model with image preprocessing techniques to extract information from Korean-language invoices, achieving an 87% F1-score with negligible processing time, according to arXiv paper 2609.35796v1. The work targets Korean, the native language of about 80 million people, and was assessed on a rich set of collected invoices. The model is intended to automate extraction of purchased items, time, and total money from commercial documents.

by read1 min views1 publishedSep 30, 2026

arXiv:2609.35796v1 Announce Type: new 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.

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