Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review
This is an incremental review paper that consolidates existing knowledge for researchers in the field of text-rich image understanding.
The paper presents a systematic survey of multimodal large language models for text-rich image understanding, reviewing their timeline, architecture, and performance on benchmarks to facilitate further research.
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.