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

PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend engine="transformers" 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 What changed? 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 . In practice, this means: - 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 throughengine_config . A simple way to understand the stack: 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. Why this matters 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. For developers, this means less integration friction and a more natural path from documents to downstream RAG, agent, search, analytics, or automation workflows. Quick start Install PaddleOCR 3.5, PaddleX, Transformers, and a compatible PyTorch build for your hardware. For example, on a CUDA 12.6 environment: python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 python -m pip install "paddleocr==3.5.0" "paddlex==3.5.2" "transformers>=5.4.0" For CPU, ROCm, or other environments, install the PyTorch build that matches your target hardware. Run from the command line: paddleocr ocr \ -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png \ --device gpu:0 \ --engine transformers Or use the Python API: from paddleocr import PaddleOCR pipeline = PaddleOCR( device="gpu:0", engine="transformers", use_doc_orientation_classify=False, use_doc_unwarping=False, use_textline_orientation=False, engine_config={ "dtype": "float32", }, ) results = pipeline.predict( "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png" ) for result in results: print(result) The Hugging Face Space uses float32 for broad compatibility. For your own hardware, you can tune backend-specific options through engine_config : engine_config = { "dtype": "bfloat16", "device_type": "gpu", "device_id": 0, "attn_implementation": "sdpa", } The best configuration depends on your model, hardware, and deployment environment. When should you use the Transformers backend? Use the Transformers backend when you want PaddleOCR’s OCR and document parsing capabilities to fit more…