PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

🔥 Official Website: Baidu AI Studio | 📝 arXiv: Technical Report

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. This is the official transformers weights for PaddleOCR-VL.

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formulas, and charts, making it suitable for a wide range of challenging content types, including handwritten text and historical documents.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

Usage

[!NOTE] This model should be used with PPDocLayoutV3. Find an end-to-end example inference in this notebook.

Make sure to have transformers above v5.

python -m pip install "transformers>=5.0.0"

You can load the model as follows. Since you need to have the detected regions, please refer to notebook for complete inference.

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
model_id = "PaddlePaddle/PaddleOCR-VL-hf"

processor = AutoProcessor.from_pretrained(ocr_model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id, torch_dtype=torch.bfloat16
).to(device)

def ocr_region(crop, prompt):
    """Run PaddleOCR-VL on a single cropped region."""
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": crop},
                {"type": "text", "text": prompt},
            ],
        }
    ]
    inputs = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    ).to(ocr_model.device)

    generated_ids = model.generate(**inputs, max_new_tokens=1024)
    trimmed = generated_ids[0][inputs["input_ids"].shape[-1] :]
    return processor.decode(trimmed, skip_special_tokens=True)

parsed_regions = []

# assuming you have detected regions from PPDocLayoutv3 in detections

for det in detections:
    label = det["label"]
    prompt = LABEL_TO_PROMPT.get(label)

    x1, y1, x2, y2 = det["box"]
    crop = image.crop((x1, y1, x2, y2))
    text = recognise_region(crop, prompt)

    parsed_regions.append({**det, "prompt": prompt, "text": text})
    print(f"[{det['order']}] {label}  prompt={prompt}")
    print(text)

Performance

Page-Level Document Parsing

1. OmniDocBench v1.5

PaddleOCR-VL achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5

2. OmniDocBench v1.0

PaddleOCR-VL achieves SOTA performance for almost all metrics of overall, text, formula, tables and reading order on OmniDocBench v1.0

Notes:

• The metrics are from MinerU, OmniDocBench, and our own internal evaluations.

Element-level Recognition

1. Text

Comparison of OmniDocBench-OCR-block Performance

PaddleOCR-VL’s robust and versatile capability in handling diverse document types, establishing it as the leading method in the OmniDocBench-OCR-block performance evaluation.

Comparison of In-house-OCR Performance

In-house-OCR provides a evaluation of performance across multiple languages and text types. Our model demonstrates outstanding accuracy with the lowest edit distances in all evaluated scripts.

2. Table

Comparison of In-house-Table Performance

Our self-built evaluation set contains diverse types of table images, such as Chinese, English, mixed Chinese-English, and tables with various characteristics like full, partial, or no borders, book/manual formats, lists, academic papers, merged cells, as well as low-quality, watermarked, etc. PaddleOCR-VL achieves remarkable performance across all categories.

3. Formula

Comparison of In-house-Formula Performance

In-house-Formula evaluation set contains simple prints, complex prints, camera scans, and handwritten formulas. PaddleOCR-VL demonstrates the best performance in every category.

4. Chart

Comparison of In-house-Chart Performance

The evaluation set is broadly categorized into 11 chart categories, including bar-line hybrid, pie, 100% stacked bar, area, bar, bubble, histogram, line, scatterplot, stacked area, and stacked bar. PaddleOCR-VL not only outperforms expert OCR VLMs but also surpasses some 72B-level multimodal language models.

Visualization

Comprehensive Document Parsing

Text

Table

Formula

Chart

Acknowledgments

We would like to thank ERNIE, Keye, MinerU, OmniDocBench for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project!

Citation

If you find PaddleOCR-VL helpful, feel free to give us a star and citation.

@misc{cui2025paddleocrvlboostingmultilingualdocument,
      title={PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model}, 
      author={Cheng Cui and Ting Sun and Suyin Liang and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Xueqing Wang and Changda Zhou and Hongen Liu and Manhui Lin and Yue Zhang and Yubo Zhang and Handong Zheng and Jing Zhang and Jun Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
      year={2025},
      eprint={2510.14528},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.14528}, 
}