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

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:

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 through engine_config.

A simple way to understand the stack:

Layer	What it means	Examples
Application layer	Applications that use OCR and document To finish reading, please visit source site Categories Categories Search for: Recent Posts The Open Agent Leaderboard PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend Introducing the Ettin Reranker Family OlmoEarth v1.1: A more efficient family of Earth observation models Specialization Beats Scale: A Strategic Variable Most AI Procurement Decisions Overlook Tags Attention blogathon Calculus Command-line Tools Data Preparation data science data visualization Deep Learning Deep Learning for Computer Vision Deep Learning for Natural Language Processing Deep Learning for Time Series Deep Learning Performance Deep Learning with PyTorch Ensemble Learning Generative Adversarial Networks Imbalanced Classification Linear Algebra Long Short-Term Memory Networks machine learning Machine Learning Algorithms Machine Learning Process Machine Learning Resources machine translation Matplotlib Natural language processing Natural Language Processing & Speech Neural MT nlp NMT opencv Optimization pandas Probability python Python for Machine Learning Python Machine Learning Resources R Machine Learning scikit-learn sentiment analysis Start Machine Learning Statistics Time Series Weka Machine Learning XGBoost Categories Categories Archives Archives Powered by WordPress and Rubine.

Layer

What it means

Examples

Application layer

Applications that use OCR and document

To finish reading, please visit source site