PaddleOCR-VL

Introduction

PaddleOCR-VL is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.

Core Features

1. Compact yet Powerful VLM Architecture: We present a novel vision-language model that is specifically designed for resource-efficient inference, achieving outstanding performance in element recognition. By integrating a NaViT-style dynamic high-resolution visual encoder with the lightweight ERNIE-4.5-0.3B language model, we significantly enhance the model’s recognition capabilities and decoding efficiency. This integration maintains high accuracy while reducing computational demands, making it well-suited for efficient and practical document processing applications.

2. SOTA Performance on Document Parsing: PaddleOCR-VL achieves state-of-the-art performance in both page-level document parsing and element-level recognition. It significantly outperforms existing pipeline-based solutions and exhibiting strong competitiveness against leading vision-language models (VLMs) in document parsing. Moreover, it excels in recognizing complex document elements, such as text, tables, formulas, and charts, making it suitable for a wide range of challenging content types, including handwritten text and historical documents. This makes it highly versatile and suitable for a wide range of document types and scenarios.

3. Multilingual Support: PaddleOCR-VL Supports 109 languages, covering major global languages, including but not limited to Chinese, English, Japanese, Latin, and Korean, as well as languages with different scripts and structures, such as Russian (Cyrillic script), Arabic, Hindi (Devanagari script), and Thai. This broad language coverage substantially enhances the applicability of our system to multilingual and globalized document processing scenarios.

Model Architecture

News

• 2025.11.07 🚀 Enabled flash-attn in the transformers library to achieve faster inference with PaddleOCR-VL-0.9B.
• 2025.11.04 🌟 PaddleOCR-VL-0.9B is now officially supported on vLLM .
• 2025.10.29 🤗 Supports calling the core module PaddleOCR-VL-0.9B of PaddleOCR-VL via the transformers library.
• 2025.10.16 🚀 We release PaddleOCR-VL, — a multilingual documents parsing via a 0.9B Ultra-Compact Vision-Language Model with SOTA performance.

Usage

Install Dependencies

Install PaddlePaddle and PaddleOCR:

# The following command installs the PaddlePaddle version for CUDA 12.6. For other CUDA versions and the CPU version, please refer to https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html
python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
python -m pip install -U "paddleocr[doc-parser]>=3.4.0"

Basic Usage

CLI usage:

paddleocr doc_parser -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png --pipeline_version v1

Python API usage:

from paddleocr import PaddleOCRVL
pipeline = PaddleOCRVL(pipeline_version="v1")
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
for res in output:
    res.print()
    res.save_to_json(save_path="output")
    res.save_to_markdown(save_path="output")

Accelerate VLM Inference via Optimized Inference Servers

1. Start the VLM inference server:

   You can start the vLLM inference service using one of two methods:

   • Method 1: PaddleOCR method

     docker run \
         --rm \
         --gpus all \
         --network host \
         ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu \
         paddleocr genai_server --model_name PaddleOCR-VL-0.9B --host 0.0.0.0 --port 8080 --backend vllm
     

   • Method 2: vLLM method

     vLLM: PaddleOCR-VL Usage Guide

2. Call the PaddleOCR CLI or Python API:

   paddleocr doc_parser \
       -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \
       --pipeline_version v1 \
       --vl_rec_backend vllm-server \
       --vl_rec_server_url http://127.0.0.1:8080/v1