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

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.

Figures (46)

• Figure 1: PaddleOCR-VL achieves state-of-the-art performance with the fewest vision tokens and parameters on OmniDocBench v1.5.
• Figure 2: Architectural comparison of End-to-end VLM and our method. Among various types of document images, the valid area accounts for less than 50% on average. Therefore, our proposed PaddleOCR-VL can achieve improvements in visual information processing by discarding redundant regions.
• Figure 3: The overview of our proposed PaddleOCR-VL, which consists of two components. The first component is VRFM, which accurately extracts valid regions containing layout elements from unstructured document images and predicts the reading order. Subsequently, each valid region is fed into our designed PaddleOCR-VL-0.9B for precise recognition of individual elements. The final results are then reconstructed into a structured document according to the reading order.
• Figure 4: Architecture of the Valid Region Focus Module (VRFM).
• Figure S1: The construction method and characteristics of the text training data for PaddleOCR-VL-0.9B.