Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review

ACL ARR 2025 February Submission3506 Authors

15 Feb 2025 (modified: 09 May 2025)ACL ARR 2025 February SubmissionEveryoneRevisionsBibTeXCC BY 4.0
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.

Paper Type: Long
Research Area: Discourse and Pragmatics
Research Area Keywords: MLLM, Text-rich Image understanding, OCR
Contribution Types: Surveys
Languages Studied: English, Chinese
Submission Number: 3506
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