发表机构
Institute of Information Engineering, Chinese Academy of Sciences; Large Language Model Department, Tencent; Nankai University（中国科学院信息工程研究所; 腾讯大语言模型部; 南开大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
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。
AI 中文摘要
我们展示了HunyuanOCR-1.5，一个轻量级端到端OCR专用视觉语言模型。它统一了文档解析、文本识别、信息提取、文本-图像翻译和多图像文档理解。在HunyuanOCR-1.0架构基础上，提升效率和能力，在多任务场景表现出色，还将发布模型权重和训练代码。
英文摘要
We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the lightweight architecture of HunyuanOCR-1.0, HunyuanOCR-1.5 does not redesign the backbone, but systematically improves both efficiency and capability. For efficiency, we adapt DFlash to OCR decoding, significantly reducing the latency of long structured outputs such as dense documents, tables, and formulas while preserving output distribution. Powered by DFlash, HunyuanOCR-1.5 achieves a 6.37x Transformer inference speedup and a 2.14x speedup under vLLM, delivering the fastest inference among lightweight OCR VLMs. For capability, we propose Agentic Data Flow, an agent-driven data construction system that transforms model weaknesses into executable data requirements and autonomously performs material search, quality verification, and pipeline development. It substantially improves long-tail capabilities in ancient-script OCR, fine-grained chart and table parsing, multi-image text-centric QA, low-resource multilingual parsing, and document hallucination evaluation. HunyuanOCR-1.5 ranks among the top-tier end-to-end OCR solutions on OmniDocBench v1.6 while achieving new performance milestones across these long-tail tasks. Combined with an upgraded pretraining and post-training recipe, HunyuanOCR-1.5 further extends its capability in high-resolution, long-context, and multi-task scenarios. Experiments demonstrate faster inference, broader OCR capability coverage, and the deployment advantages of a lightweight end-to-end model. We will release the model weights and training code to support future research and real-world OCR applications.
