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Main Authors: Lee, JoonHo, Park, Sunho
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2510.02543
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author Lee, JoonHo
Park, Sunho
author_facet Lee, JoonHo
Park, Sunho
contents We investigate OCR-augmented generation with Vision Language Models (VLMs), exploring tasks in Korean and English toward multilingualism. To support research in this domain, we train and release KLOCR, a strong bilingual OCR baseline trained on 100M instances to augment VLMs with OCR ability. To complement existing VQA benchmarks, we curate KOCRBench for Korean VQA, and analyze different prompting methods. Extensive experiments show that OCR-extracted text significantly boosts performance across open source and commercial models. Our work offers new insights into OCR-augmented generation for bilingual VQA. Model, code, and data are available at https://github.com/JHLee0513/KLOCR.
format Preprint
id arxiv_https___arxiv_org_abs_2510_02543
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Exploring OCR-augmented Generation for Bilingual VQA
Lee, JoonHo
Park, Sunho
Computer Vision and Pattern Recognition
We investigate OCR-augmented generation with Vision Language Models (VLMs), exploring tasks in Korean and English toward multilingualism. To support research in this domain, we train and release KLOCR, a strong bilingual OCR baseline trained on 100M instances to augment VLMs with OCR ability. To complement existing VQA benchmarks, we curate KOCRBench for Korean VQA, and analyze different prompting methods. Extensive experiments show that OCR-extracted text significantly boosts performance across open source and commercial models. Our work offers new insights into OCR-augmented generation for bilingual VQA. Model, code, and data are available at https://github.com/JHLee0513/KLOCR.
title Exploring OCR-augmented Generation for Bilingual VQA
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2510.02543