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English(EN) PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks

PP-OCRv6 轻量级OCR系统性能超越大型VLMs

新开发的OCR系统PP-OCRv6提供了多种模型层级,适用于从服务器到边缘设备的各种部署场景。该系统采用统一的MetaFormer风格构建块和以数据为中心的优化来提升性能。PP-OCRv6在准确性和检测指标上优于其前代产品PP-OCRv5,并且在参数量远少于Qwen3 VL 235B、GPT-5.5和Gemini 3.1 Pro等大型视觉语言模型(VLMs)的情况下,性能显著超越了它们。此外,PP-OCRv6的一个较小层级在标准CPU上提供了更快的推理速度,同时保持了可比的准确性。 AI

影响 为OCR任务提供更高效、更准确的解决方案,可能降低专业应用的计算成本。

排序理由 该集群描述了一篇详细介绍OCR系统及其性能基准的新研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yubo Zhang, Xueqing Wang, Manhui Lin, Yue Zhang, Penglongyi Deng, Ting Sun, Tingquan Gao, Zelun Zhang, Jiaxuan Liu, Changda Zhou, Hongen Liu, Suyin Liang, Cheng Cui, Yi Liu, Dianhai Yu, Yanjun Ma ·

    PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks

    arXiv:2606.13108v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved impressive results on general vision-language tasks, yet they suffer from hallucination, imprecise localization, and prohibitive computational cost when applied to dedicated OCR scenarios.…

  2. arXiv cs.CV TIER_1 English(EN) · Yanjun Ma ·

    PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks

    Vision-Language Models (VLMs) have achieved impressive results on general vision-language tasks, yet they suffer from hallucination, imprecise localization, and prohibitive computational cost when applied to dedicated OCR scenarios. This paper presents PP-OCRv6, a lightweight OCR…