PaddleOCR-VL-1.5

PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing

🔥 Official Website 📝 Technical Report

Introduction

PaddleOCR-VL-1.5 is an advanced next-generation model of PaddleOCR-VL, achieving a new state-of-the-art accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions—including scanning artifacts, skew, warping, screen photography, and illumination—we propose the Real5-OmniDocBench benchmark. Experimental results demonstrate that this enhanced model attains SOTA performance on the newly curated benchmark. Furthermore, we extend the model’s capabilities by incorporating seal recognition and text spotting tasks, while remaining a 0.9B ultra-compact VLM with high efficiency.

Key Capabilities of PaddleOCR-VL-1.5

1. With a parameter size of 0.9B, PaddleOCR-VL-1.5 achieves 94.5% accuracy on OmniDocBench v1.5, surpassing the previous SOTA model PaddleOCR-VL. Significant improvements are observed in table, formula, and text recognition.

2. It introduces an innovative approach to document parsing by supporting irregular-shaped localization, enabling accurate polygonal detection under skewed and warped document conditions. Evaluations across five real-world scenarios—scanning, skew, warping, screen-photography, and illumination—demonstrate superior performance over mainstream open-source and proprietary models.

3. The model introduces text spotting (text-line localization and recognition), along with seal recognition, with all corresponding metrics setting new SOTA results in their respective tasks.

4. PaddleOCR-VL-1.5 further strengthens its capability in specialized scenarios and multilingual recognition. Recognition performance is improved for rare characters, ancient texts, multilingual tables, underlines, and checkboxes, and language coverage is extended to include China's Tibetan script and Bengali.

5. The model supports automatic cross-page table merging and cross-page paragraph heading recognition, effectively mitigating content fragmentation issues in long-document parsing.

Model Architecture

News

• 2026.03.06 🚀 Support llama.cpp inference for the VLM component in PaddleOCR-VL-1.5. Click here for details.
• 2026.01.29 🚀 We release PaddleOCR-VL-1.5, —a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.

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 v1.5

Python API usage:

from paddleocr import PaddleOCRVL
pipeline = PaddleOCRVL(pipeline_version="v1.5")
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-1.5-0.9B --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 v1.5 \
       --vl_rec_backend vllm-server \
       --vl_rec_server_url http://127.0.0.1:8080/v1
   

   from paddleocr import PaddleOCRVL
   pipeline = PaddleOCRVL(pipeline_version="v1.5", vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8080/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")
   

For more usage details and parameter explanations, see the documentation.

PaddleOCR-VL-1.5-0.9B Usage with transformers

Currently, the PaddleOCR-VL-1.5-0.9B model facilitates seamless inference via the transformers library, supporting comprehensive text spotting and the recogniti