发表机构
China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd.（中国电信人工智能技术（北京）有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有文档解析方法的两大挑战，本文提出NaviDC-OCR框架，通过形变感知学习、自适应采样及内容-结构解耦学习策略，在多基准测试中取得最优性能并获ICDAR 2026相关挑战第一。
AI 中文摘要
文档解析旨在将非结构化文档转换为结构化、可被机器读取的表示形式。视觉语言模型（VLM）的近期进展大幅推动了文档解析技术，但现有方法仍面临两大核心挑战：其一，基于解耦VLM的方法高度依赖准确的布局分析，而相机拍摄文档中的几何畸变会引发级联误差；其二，尽管端到端VLM方法降低了对显式布局检测的依赖，但在高分辨率场景中常出现冗余生成、幻觉及结构推理不足的问题。为应对上述挑战，本文提出统一文档解析框架NaviDC-OCR，该框架引入形变感知学习以将几何感知融入VLM，并针对复杂布局表示提出自适应采样机制；此外，开发了内容-结构解耦学习策略，以显式建模公式语法与表格结构，实现更高效的结构化表示学习。大量实验表明，NaviDC-OCR在各类文档解析基准上均取得最优性能：在OmniDocBench v1.6、Wild-OmniDocBench、PureDocBench上分别获得96.87、88.53、78.41的整体得分，且在ICDAR 2026 Sci-ImageMiner挑战赛中排名第一，验证了NaviDC-OCR在复杂文档解析场景中的有效性与泛化能力。
英文摘要
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 TeleOCR, a unified framework for document parsing. TeleOCR 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 TeleOCR 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 TeleOCR in complex document parsing scenarios.
