Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation

• URL: http://arxiv.org/abs/2602.21956v1
• Date: Wed, 25 Feb 2026 14:38:47 GMT
• Title: Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation
• Authors: Junxin Lu, Tengfei Song, Zhanglin Wu, Pengfei Li, Xiaowei Liang, Hui Yang, Kun Chen, Ning Xie, Yunfei Lu, Jing Zhao, Shiliang Sun, Daimeng Wei,
• Abstract summary: Text Image Machine Translation aims to translate text embedded in images in source-language into target-language.<n>Existing TIMT methods struggle with high-resolution text-rich images due to cluttered layouts, diverse fonts, and non-textual distractions.<n>We propose GLoTran, a global-local dual visual perception framework for MLLM-based TIMT.
• Score: 39.52909851398792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text Image Machine Translation (TIMT) aims to translate text embedded in images in the source-language into target-language, requiring synergistic integration of visual perception and linguistic understanding. Existing TIMT methods, whether cascaded pipelines or end-to-end multimodal large language models (MLLMs),struggle with high-resolution text-rich images due to cluttered layouts, diverse fonts, and non-textual distractions, resulting in text omission, semantic drift, and contextual inconsistency. To address these challenges, we propose GLoTran, a global-local dual visual perception framework for MLLM-based TIMT. GLoTran integrates a low-resolution global image with multi-scale region-level text image slices under an instruction-guided alignment strategy, conditioning MLLMs to maintain scene-level contextual consistency while faithfully capturing fine-grained textual details. Moreover, to realize this dual-perception paradigm, we construct GLoD, a large-scale text-rich TIMT dataset comprising 510K high-resolution global-local image-text pairs covering diverse real-world scenarios. Extensive experiments demonstrate that GLoTran substantially improves translation completeness and accuracy over state-of-the-art MLLMs, offering a new paradigm for fine-grained TIMT under high-resolution and text-rich conditions.

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