Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang
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.
BibTeX
@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://aclanthology.org/2025.findings-acl.1023/",
doi = "10.18653/v1/2025.findings-acl.1023",
pages = "19941--19958",
ISBN = "979-8-89176-256-5"
}