Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916626160418816 |
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| author | Fu, Pei Guan, Tongkun Wang, Zining Guo, Zhentao Duan, Chen Sun, Hao Chen, Boming Ma, Jiayao Jiang, Qianyi Zhou, Kai Luo, Junfeng |
| author_facet | Fu, Pei Guan, Tongkun Wang, Zining Guo, Zhentao Duan, Chen Sun, Hao Chen, Boming Ma, Jiayao Jiang, Qianyi Zhou, Kai Luo, Junfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review Fu, Pei Guan, Tongkun Wang, Zining Guo, Zhentao Duan, Chen Sun, Hao Chen, Boming Ma, Jiayao Jiang, Qianyi Zhou, Kai Luo, Junfeng Computer Vision and Pattern Recognition 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. |
| title | Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.16586 |