@inproceedings{fu-etal-2025-multimodal,
    title = "Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review",
    author = "Fu, Pei  and
      Guan, Tongkun  and
      Wang, Zining  and
      Guo, Zhentao  and
      Duan, Chen  and
      Sun, Hao  and
      Chen, Boming  and
      Jiang, Qianyi  and
      Ma, Jiayao  and
      Zhou, Kai  and
      Luo, Junfeng",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1023/",
    pages = "19941--19958",
    ISBN = "979-8-89176-256-5",
    abstract = "The recent emergence of Multi-modal Large Language Models (MLLMs) has introduced a new dimension to the Text-rich Image Understanding (TIU) field, with models demonstrating impressive and inspiring performance. However, their rapid evolution and widespread adoption have made it increasingly challenging to keep up with the latest advancements. To address this, we present a systematic and comprehensive survey to facilitate further research on TIU MLLMs. Initially, we outline the timeline, architecture, and pipeline of nearly all TIU MLLMs. Then, we review the performance of selected models on mainstream benchmarks. Finally, we explore promising directions, challenges, and limitations within the field."
}