Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding
This addresses document understanding for users needing efficient processing without OCR, but it is incremental as it builds on existing MLLM frameworks.
The paper tackles the problem of OCR-free document understanding by proposing a Hierarchical Visual Feature Aggregation module to reduce input tokens for Multimodal Large Language Models, achieving superior performance in various tasks.
We present a novel OCR-free document understanding framework based on pretrained Multimodal Large Language Models (MLLMs). Our approach employs multi-scale visual features to effectively handle various font sizes within document images. To address the increasing costs of considering the multi-scale visual inputs for MLLMs, we propose the Hierarchical Visual Feature Aggregation (HVFA) module, designed to reduce the number of input tokens to LLMs. Leveraging a feature pyramid with cross-attentive pooling, our approach effectively manages the trade-off between information loss and efficiency without being affected by varying document image sizes. Furthermore, we introduce a novel instruction tuning task, which facilitates the model's text-reading capability by learning to predict the relative positions of input text, eventually minimizing the risk of truncated text caused by the limited capacity of LLMs. Comprehensive experiments validate the effectiveness of our approach, demonstrating superior performance in various document understanding tasks.