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

Abstract

Text Image Machine Translation (TIMT) aims to translatetext embedded in images in the source-language into target-language,requiring synergistic integration of visual perception and linguistic un-derstanding. Existing TIMT methods, whether cascaded pipelines orend-to-end multimodal large language models (MLLMs), struggle withhigh-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 proposeGLoTran, a global-local dual visual perception framework for MLLM-based TIMT. GLoTran integrates a low-resolution global image withmulti-scale region-level text image slices through pre-local region trans-lation replay and an instruction-guided alignment strategy, conditioningMLLMs to maintain scene-level contextual consistency while faithfullycapturing fine-grained textual details. Moreover, to realize this dual-perception paradigm, we construct GLoD, a large-scale text-rich TIMTdataset comprising 510K high-resolution global-local image-text pairscovering diverse real-world scenarios. Extensive experiments demonstratethat GLoTran substantially improves translation completeness and ac-curacy over state-of-the-art MLLMs.