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2026-05-18 0 浏览 会员

趋势解读:PaddleOCR 3.5,聚焦 Agent 工作流自动化

PaddleOCR 3.5 将 OCR 和文档解析模型与 Hugging Face Transformers 集成,为开发者提供更灵活的推理后端选择,助力文档 AI 工作流。

SOURCE / AI技能杠杆 MIN / 9 ACCESS / 会员 POST / 2026-05-18 23:12:46

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作者:Hugging Face Blog 来源站点:huggingface.co 原贴时间:

原文

PaddleOCR 3.5 brings OCR and document parsing tasks closer to the Hugging Face ecosystem. With this release, supported PaddleOCR models can run with Hugging Face Transformers as an inference backend by setting: PaddleOCR continues to provide OCR model series such as PP-OCRv5 and document parsing model series such as PaddleOCR-VL 1.5 , while Transformers becomes one of the supported backends for running them. Try the live demo on Hugging Face Spaces: https://huggingface.co/spaces/PaddlePaddle/paddleocr-3.5-transformers-demo PaddleOCR 3.5 introduces a more flexible inference-engine interface. Developers can select the backend through the engine parameter and pass backend-specific options through engine_config . The pipelines behind these tasks are managed by PaddleOCR, so developers do not need to manually call each internal component. Transformers becomes one of the supported inference backends for running supported PaddleOCR models. Developers can configure backend-related options such as dtype , device placement, and attention implementation through engine_config . This release is mainly about the inference backend layer: PaddleOCR continues to provide OCR and document parsing capabilities, while Transformers gives supported PaddleOCR models another backend option that fits naturally into Hugging Face-centered environments. The larger Document AI workflow remains in the hands of developers and application builders. For RAG, Document AI, and document agent applications, the hard part often starts before the LLM. Developers first need to turn PDFs, scanned documents, screenshots, tables, charts, formulas, and complex page layouts into reliable structured data. If this ingestion step is weak, the downstream LLM workflow may miss key information, retrieve the wrong context, or produce unreliable answers. PaddleOCR helps address this document ingestion challenge by providing OCR series models such as PP-OCRv5 and document parsing series models such as PaddleOCR-VL-1.5. With PaddleOCR 3.5, these capabilities are now easier to connect with Transformers-centered stacks. Supported PaddleOCR models can run with a Transformers backend, while PaddleOCR continues to manage the OCR or document parsing pipeline behind the scenes.

中文翻译

PaddleOCR 3.5 将 OCR 和文档解析任务更紧密地连接到 Hugging Face 生态系统。通过这次发布,支持的 PaddleOCR 模型可以通过设置使用 Hugging Face Transformers 作为推理后端运行:PaddleOCR 继续提供诸如 PP-OCRv5 的 OCR 模型系列和诸如 PaddleOCR-VL 1.5 的文档解析模型系列,而 Transformers 成为运行它们支持的后端之一。在 Hugging Face Spaces 上尝试实时演示:https://huggingface.co/spaces/PaddlePaddle/paddleocr-3.5-transformers-demo PaddleOCR 3.5 引入了更灵活的推理引擎接口。开发者可以通过 engine 参数选择后端,并通过 engine_config 传递后端特定选项。这些任务背后的管道由 PaddleOCR 管理,因此开发者无需手动调用每个内部组件。Transformers 成为运行支持的 PaddleOCR 模型的推理后端之一。开发者可以通过 engine_config 配置后端相关选项,如 dtype、设备放置和注意力实现。本次发布主要涉及推理后端层:PaddleOCR 继续提供 OCR 和文档解析能力,而 Transformers 为支持的 PaddleOCR 模型提供了另一个适合 Hugging Face 中心环境的后端选项。更大的文档 AI 工作流仍然掌握在开发者和应用构建者手中。对于 RAG、文档 AI 和文档代理应用,困难部分通常在 LLM 之前开始。开发者首先需要将 PDF、扫描文档、截图、表格、图表、公式和复杂页面布局转换为可靠的结构化数据。如果这一摄取步骤薄弱,下游 LLM 工作流可能会错过关键信息、检索错误上下文或产生不可靠的答案。PaddleOCR 通过提供 OCR 系列模型如 PP-OCRv5 和文档解析系列模型如 PaddleOCR-VL-1.5 来帮助解决这一文档摄取挑战。借助 PaddleOCR 3.5,这些能力现在更易于与以 Transformers 为中心的堆栈连接。支持的 PaddleOCR 模型可以运行在 Transformers 后端,而 PaddleOCR 在后台继续管理 OCR 或文档解析管道。

核心信息

PaddleOCR 3.5 将 OCR 和文档解析模型与 Hugging Face Transformers 集成,为开发者提供更灵活的推理后端选择,助力文档 AI 工作流。

  • PaddleOCR 3.5 集成 Hugging Face Transformers 作为推理后端。
  • 支持 PP-OCRv5 和 PaddleOCR-VL 1.5 模型系列。
  • 开发者可通过 engine_config 灵活配置后端参数。
  • 降低文档 AI 工作流中数据摄入的复杂度。
  • 适合在 Transformers 生态中构建 RAG 和文档代理应用。
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