NaviDC-OCR：跨越数字与相机拍摄文档的文档解析

摘要

文档解析旨在将非结构化文档转换为结构化且机器可读的表示形式。近年来，视觉语言模型（VLM）的进展显著推动了文档解析技术的发展。然而，现有方法仍面临两大挑战。首先，基于解耦式VLM的方法严重依赖精确的版面分析，而相机拍摄文档中的几何畸变可能引入级联误差。其次，尽管基于端到端VLM的方法缓解了对显式版面检测的依赖，但在高分辨率场景下，它们往往存在冗余生成、幻觉以及结构化推理能力不足等问题。为应对这些挑战，我们提出NaviDC-OCR，一个统一的文档解析框架。NaviDC-OCR引入形变感知学习，将几何感知能力融入VLM，并提出一种自适应采样机制用于复杂版面表示。此外，我们设计了一种内容-结构解耦学习策略，显式建模公式语法和表格结构，从而实现更高效的结构化表示学习。大量实验表明，NaviDC-OCR在多个文档解析基准上取得了当前最优性能。其在OmniDocBench v1.6、Wild-OmniDocBench和PureDocBench上分别获得96.87、88.53和78.41的综合得分，并在ICDAR 2026 Sci-ImageMiner挑战赛中排名第一。这些结果验证了NaviDC-OCR在复杂文档解析场景中的有效性和泛化能力。

English

Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly advanced document parsing. However, existing approaches still face two major challenges. First, decoupled VLM-based methods heavily rely on accurate layout analysis, where geometric distortions in camera-captured documents can introduce cascading errors. Second, although end-to-end VLM-based methods alleviate the dependence on explicit layout detection, they often suffer from redundant generation, hallucinations, and insufficient structural reasoning in high-resolution scenarios. To address these challenges, we propose NaviDC-OCR, a unified framework for document parsing. NaviDC-OCR introduces deformation-aware learning to incorporate geometric perception into VLMs and proposes an adaptive sampling mechanism for complex layout representation. Furthermore, a content-structure decoupled learning strategy is developed to explicitly model formula grammars and table structures, enabling more effective structured representation learning. Extensive experiments demonstrate that NaviDC-OCR achieves state-of-the-art performance across diverse document parsing benchmarks. It obtains overall scores of 96.87, 88.53 and 78.41 on OmniDocBench v1.6, Wild-OmniDocBench, and PureDocBench, respectively, and ranks first in the ICDAR 2026 Sci-ImageMiner Challenge. These results validate the effectiveness and generalization capability of NaviDC-OCR in complex document parsing scenarios.