浑元OCR技术报告

摘要

本文提出HunYuanOCR——一个商用级、开源轻量（10亿参数）的OCR专用视觉语言模型。该模型采用原生视觉Transformer（ViT）与轻量化大语言模型通过MLP适配器连接的架构，在OCR任务中展现出超越商业API、传统流水线及更大参数量模型（如Qwen3-VL-4B）的卓越性能。具体而言，模型在感知任务（文本检测与解析）上优于当前公开方案，在语义任务（信息抽取、图文翻译）中表现突出，荣获ICDAR 2025 DIMT挑战赛小模型赛道冠军，并在参数量小于30亿的视觉语言模型中取得OCRBench基准最优成绩。 HunYuanOCR实现三大突破：1）通用性与高效性统一：在轻量化框架内集成检测、解析、信息抽取、视觉问答及翻译等核心能力，克服了专用OCR模型能力单一与通用视觉语言模型效率低下的局限；2）端到端架构革新：采用纯端到端范式摆脱了对预处理模块（如版面分析）的依赖，从根本上解决传统流水线的误差传递问题并简化系统部署；3）数据与强化学习协同：验证了高质量数据的关键作用，并首次在业界证明强化学习策略可为OCR任务带来显著性能提升。模型已在HuggingFace平台开源，同时提供基于vLLM的高性能部署方案，其推理效率达到业界领先水平。我们期待该模型能推动前沿技术探索，并为工业应用提供坚实基础。

English

This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters. HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow "OCR expert models" and inefficient "General VLMs". 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks. HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications.

浑元OCR技术报告

HunyuanOCR Technical Report

摘要

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