Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exact matching. Existing OCR systems and general-purpose vision-language models often fail to reliably parse such documents. We propose MeDocVL, a post-trained vision-language model for query-driven medical document parsing. Our framework combines Training-driven Label Refinement to construct high-quality supervision from noisy annotations, with a Noise-aware Hybrid Post-training strategy that integrates reinforcement learning and supervised fine-tuning to achieve robust and precise extraction. Experiments on medical invoice benchmarks show that MeDocVL consistently outperforms conventional OCR systems and strong VLM baselines, achieving state-of-the-art performance under noisy supervision.
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Tencent（腾讯）；Nankai University（南开大学）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University of Science and Technology（南京理工大学）
Parser-Oriented Structural Refinement for a Stable Layout Interface in Document Parsing
Unisound AI Technology Co., Ltd.（云知声智能科技股份有限公司）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统实验室）
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院（SIMTech），新加坡科技研究局（A*STAR））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心（ARTC），新加坡科技研究局（A*STAR））；Nanyang Technological University（南洋理工大学）
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Large Language Model Department, Tencent（腾讯大语言模型部）；Nankai University（南开大学）
研究改进轻量级端到端OCR视觉语言模型。基于HunyuanOCR-1.0架构，用DFlash提升效率，提出Agentic Data Flow增强能力，在多任务表现出色，还将发布模型权重和代码。
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
School of Information Science and Engineering, East China University of Science and Technology（东华大学信息科学与工程学院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；Alibaba Group（阿里巴巴集团）
HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding
South China University of Technology（华南理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；Nanjing University（南京大学）
P-MTP: Efficient Document Parsing via Multi-Token Prediction with Progressive Depth Scaling
Department of Computer Vision Technology (VIS) Baidu Inc China（百度计算机视觉技术部（VIS））；Tsinghua University Shenzhen International Graduate School China（清华大学深圳国际研究生院）
DocParser: Hierarchical Structure Parsing of Document Renderings
ETH Zurich（苏黎世联邦理工学院）
翻译渲染（e. G. PDF、扫描）转换为分层文档结构，这在许多实际应用程序的日常工作中有着广泛的需求。然而，一个整体的，原则性的方法来推断完整的层次结构的文件是失踪。作为补救措施，我们开发了“DocParser”：一个端到端的系统，用于解析完整的文档结构-包括所有文本元素，嵌套的图形，表格和表格单元格结构。我们的第二个贡献是提供了一个数据集，用于评估分层文档结构解析。我们的第三个贡献是提出了一个可扩展的学习框架设置特定领域的数据是稀缺的，我们解决了一种新的方法，以弱监督，显着提高了文档结构解析性能。我们的实验证实了我们提出的弱监督的有效性：与没有弱监督的基线相比，它将检测文档实体的平均精度提高了39.1%，将分类层次关系的F1得分提高了35.8%。
How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean, Degraded, and Real-World Settings
CASIA（中国科学院自动化研究所）；UCAS（中国科学技术大学）；NWPU（西北工业大学）；JLU（吉林大学）；USTC（中国科学技术大学）；PKU（北京大学）；HelloGroup；ShanghaiTech（上海科技大学）；Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information（北京多模态信息超级智能安全重点实验室）
