Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing

TL;DR

This paper introduces PaddleOCR-VL, a coarse-to-fine visual processing architecture that enhances document parsing efficiency and accuracy by focusing on relevant regions and reducing redundant visual information.

Contribution

It proposes a novel VRFM module and a compact 0.9B vision-language model to improve document parsing performance while significantly reducing computational costs.

Findings

Achieves state-of-the-art performance in page-level and element-level recognition.

Outperforms existing solutions in efficiency and accuracy.

Utilizes fewer vision tokens and parameters for fast inference.

Abstract

Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs. We attribute this inefficiency to substantial visual regions redundancy in document images, like background. To tackle this, we propose PaddleOCR-VL, a novel coarse-to-fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance. Specifically, we introduce a lightweight Valid Region Focus Module (VRFM) which leverages localization and contextual relationship prediction capabilities to identify valid vision tokens. Subsequently, we design and train a compact yet powerful 0.9B vision-language model (PaddleOCR-VL-0.9B) to perform detailed recognition, guided by VRFM outputs to avoid direct processing of the entire large image. Extensive experiments demonstrate that PaddleOCR-VL achieves state-of-the-art performance in both page-level parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference while utilizing substantially fewer vision tokens and parameters, highlighting the effectiveness of targeted coarse-to-fine parsing for accurate and efficient document understanding. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.