面向文档理解的令牌级文本图像基础模型

摘要

近年来，通用视觉基础模型（VFMs）的应用日益广泛，尤其是在作为流行多模态大语言模型（MLLMs）的图像编码器方面。然而，由于缺乏语义细粒度的监督，这些模型在下游与文本图像相关的任务中——即对包含细小密集文本图像的感知、理解与推理——仍面临根本性的预测错误。为填补这一空白，我们开发了TokenOCR，这是首个专为文本图像相关任务定制的令牌级视觉基础模型，旨在支持多种传统下游应用。为促进TokenOCR的预训练，我们还设计了一套高质量的数据生成流程，构建了首个令牌级图像文本数据集TokenIT，包含2000万张图像和18亿个令牌-掩码对。此外，凭借这一具备卓越图像即文本能力的基础，我们无缝地将TokenOCR替代先前的VFMs，构建了面向基于VQA的文档理解任务的文档级MLLM——TokenVL。最终，大量实验验证了TokenOCR与TokenVL的有效性。代码、数据集及权重将发布于https://token-family.github.io/TokenOCR_project。

English

In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). However, without semantically fine-grained supervision, these models still encounter fundamental prediction errors in the context of downstream text-image-related tasks, i.e., perception, understanding and reasoning with images containing small and dense texts. To bridge this gap, we develop TokenOCR, the first token-level visual foundation model specifically tailored for text-image-related tasks, designed to support a variety of traditional downstream applications. To facilitate the pretraining of TokenOCR, we also devise a high-quality data production pipeline that constructs the first token-level image text dataset, TokenIT, comprising 20 million images and 1.8 billion token-mask pairs. Furthermore, leveraging this foundation with exceptional image-as-text capability, we seamlessly replace previous VFMs with TokenOCR to construct a document-level MLLM, TokenVL, for VQA-based document understanding tasks. Finally, extensive experiments demonstrate the effectiveness of TokenOCR and TokenVL. Code, datasets, and weights will be available at https://token-family.github.io/TokenOCR_project.

面向文档理解的令牌级文本图像基础模型

A Token-level Text Image Foundation Model for Document Understanding

摘要

Summary

Support