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PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

  • PaddleOCR 3.5 introduces a flexible inference engine interface that allows models like PP-OCRv5 and PaddleOCR-VL 1.5 to run on a Transformers backend.
  • Developers can now execute OCR and document parsing tasks by setting the engine parameter to "transformers" within the PaddleOCR pipeline.
  • This update enables native integration with Hugging Face infrastructure, allowing for custom configurations like bfloat16 precision and sdpa attention implementation.
  • The release maintains PaddleOCR's management of internal pipelines while delegating runtime execution to the Transformers library.

This release integrates PaddleOCR capabilities into the Hugging Face ecosystem to simplify document ingestion for AI pipelines.

3 min read AI-assisted

Why this matters right now

Poor document ingestion often causes RAG systems to retrieve irrelevant context or fail on complex layouts like tables and charts. Integrating PaddleOCR directly into a Transformers stack allows developers to standardize their document processing and LLM workflows within a single environment. This simplifies the path from raw PDFs to structured data, though it may not match the raw throughput speeds of the default Paddle static graph backend. Teams failing to modernize these ingestion pipelines risk maintaining fragmented infrastructure that complicates model deployment and maintenance.

How this technology has evolved

PaddleOCR 3.5 shifts from a monolithic execution model to a modular backend architecture, allowing developers to choose between Paddle-native runtimes and the Hugging Face Transformers ecosystem. By passing the engine_config parameter, users gain granular control over hardware-specific settings like device placement and data types. While this improves interoperability, the default paddle_static backend remains the preferred choice for maximum performance requirements.

FeaturePaddle Static BackendTransformers Backend
Primary Use CaseMaximum ThroughputEcosystem Integration
FrameworkPaddlePaddlePyTorch / Transformers
ConfigurationStandardengine_config (dtype, device)

What this means for your roadmap

This week

  • Review current Document AI pipelines to identify fragmentation between OCR ingestion and LLM processing.
  • Test the PaddleOCR 3.5 Transformers backend in a development environment to assess compatibility with existing PyTorch services.

This quarter

  • Benchmark the Transformers backend performance against the native Paddle static runtime to determine the optimal production configuration.
  • Standardize model artifact management by migrating supported PaddleOCR models into the Hugging Face-compatible workflow.

This year

  • Refactor document ingestion modules to use the modular engine interface, reducing dependency on custom glue code for model deployment.
  • Evaluate the feasibility of replacing legacy OCR runtimes with the unified Transformers backend to simplify infrastructure maintenance.

Sources

  1. Hugging Face: PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

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AI-assisted content: This article, PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend, was drafted using AI assistance (google/gemini-3.1-flash-lite-preview) on 18 May 2026 and reviewed by the BytesAI editorial team before publication. Verified sources: Hugging Face: PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend. Learn about our editorial process.

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