Update Vox Jot license and upstream notices for paddleocr-vl-1.5
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README.md
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license:
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tags:
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- PaddleOCR
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- PaddlePaddle
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- image-to-text
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- ocr
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- layout
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- table
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- formula
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- chart
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- seal
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- spotting
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base_model: baidu/ERNIE-4.5-0.3B-Paddle
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language:
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- en
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- zh
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- multilingual
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library_name: PaddleOCR
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---
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<div align="center">
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[](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)
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[](https://modelscope.cn/models/PaddlePaddle/PaddleOCR-VL-1.5)
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[](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo)
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[](https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo/summary)
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[](https://discord.gg/JPmZXDsEEK)
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[](https://x.com/PaddlePaddle)
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[](./LICENSE)
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/paddleocr-vl-1.5_metrics.png" width="800"/>
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</div>
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## Introduction
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**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.
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### **Key Capabilities of PaddleOCR-VL-1.5**
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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.**
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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.
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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.
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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.**
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5. The model supports **automatic cross-page table merging** and **cross-page paragraph heading recognition**, effectively mitigating content fragmentation issues in **long-document parsing.**
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### **Model Architecture**
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PaddleOCR-VL-1.5.png" width="800"/>
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</div>
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## News
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* ```2026.03.06``` 🚀 Support llama.cpp inference for the VLM component in PaddleOCR-VL-1.5. Click [here](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5-GGUF) for details.
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* ```2026.01.29``` 🚀 We release [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5), —a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.
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## Usage
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### Install Dependencies
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Install [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR):
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```bash
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# 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
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python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
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python -m pip install -U "paddleocr[doc-parser]"
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```
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> **Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors.** For macOS users, please use Docker to set up the environment.
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### Basic Usage
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CLI usage:
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```bash
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paddleocr doc_parser -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png
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```
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Python API usage:
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```python
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from paddleocr import PaddleOCRVL
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pipeline = PaddleOCRVL()
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output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
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for res in output:
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res.print()
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res.save_to_json(save_path="output")
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res.save_to_markdown(save_path="output")
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```
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### Accelerate VLM Inference via Optimized Inference Servers
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1. Start the VLM inference server:
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You can start the vLLM inference service using one of two methods:
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- Method 1: PaddleOCR method
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```bash
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docker run \
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--rm \
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--gpus all \
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--network host \
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ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu \
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paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8080 --backend vllm
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```
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- Method 2: vLLM method
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[vLLM: PaddleOCR-VL Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/PaddlePaddle/PaddleOCR-VL.html)
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2. Call the PaddleOCR CLI or Python API:
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```bash
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paddleocr doc_parser \
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-i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \
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--vl_rec_backend vllm-server \
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--vl_rec_server_url http://127.0.0.1:8080/v1
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```
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```python
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from paddleocr import PaddleOCRVL
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pipeline = PaddleOCRVL(vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8080/v1")
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output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
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for res in output:
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res.print()
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res.save_to_json(save_path="output")
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res.save_to_markdown(save_path="output")
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```
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**For more usage details and parameter explanations, see the [documentation](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html).**
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## PaddleOCR-VL-1.5-0.9B Usage with transformers
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Currently, the PaddleOCR-VL-1.5-0.9B model facilitates seamless inference via the `transformers` library, supporting **comprehensive text spotting** and the recognition of complex elements including formulas, tables, charts, and seals. Below is a simple script we provide to support inference using the PaddleOCR-VL-1.5-0.9B model with `transformers`.
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> [!NOTE]
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> Note: We currently recommend using the official method for inference, as it is faster and supports page-level document parsing. The example code below only supports element-level recognition and text spotting.
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```shell
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# ensure the transformers v5 is installed
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python -m pip install "transformers>=5.0.0"
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```
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```python
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from PIL import Image
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import torch
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from transformers import AutoProcessor, AutoModelForImageTextToText
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# ---- Settings ----
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model_path = "PaddlePaddle/PaddleOCR-VL-1.5"
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image_path = "test.png"
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task = "ocr" # Options: 'ocr' | 'table' | 'chart' | 'formula' | 'spotting' | 'seal'
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# ------------------
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# ---- Image Preprocessing For Spotting ----
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image = Image.open(image_path).convert("RGB")
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orig_w, orig_h = image.size
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spotting_upscale_threshold = 1500
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if task == "spotting" and orig_w < spotting_upscale_threshold and orig_h < spotting_upscale_threshold:
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process_w, process_h = orig_w * 2, orig_h * 2
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try:
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resample_filter = Image.Resampling.LANCZOS
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except AttributeError:
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resample_filter = Image.LANCZOS
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image = image.resize((process_w, process_h), resample_filter)
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# Set max_pixels: use 1605632 for spotting, otherwise use default ~1M pixels
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max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
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# ---------------------------
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# -------- Inference --------
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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PROMPTS = {
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"ocr": "OCR:",
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"table": "Table Recognition:",
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"formula": "Formula Recognition:",
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"chart": "Chart Recognition:",
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"spotting": "Spotting:",
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"seal": "Seal Recognition:",
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}
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model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(DEVICE).eval()
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processor = AutoProcessor.from_pretrained(model_path)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": PROMPTS[task]},
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]
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}
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]
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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images_kwargs={"size": {"shortest_edge": processor.image_processor.min_pixels, "longest_edge": max_pixels}},
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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result = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:-1])
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print(result)
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# ---------------------------
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```
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<details>
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<summary>👉 Click to expand: Use flash-attn to boost performance and reduce memory usage</summary>
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```shell
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# ensure the flash-attn2 is installed
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pip install flash-attn --no-build-isolation
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```
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```python
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model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").to(DEVICE).eval()
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```
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</details>
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## Performance
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### Document Parsing
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#### 1. OmniDocBench v1.5
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##### PaddleOCR-VL-1.5 achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/omnidocbenchv1.5_metrics.png" width="800"/>
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</div>
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> **Notes:**
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> - Performance metrics are cited from the [OmniDocBench official leaderboard](https://opendatalab.com/omnidocbench), except for Gemini-3 Pro, Qwen3-VL-235B-A22B-Instruct and our model, which were evaluated independently.
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#### 2. Real5-OmniDocBench
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##### Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.5 consistently sets new SOTA records
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/real5-omnidocbench_metrics.png" width="800"/>
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</div>
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> **Notes:**
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> - Real5-OmniDocBench is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For further details, please refer to [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
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### Inference Performance
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/inference_performance.png" width="800"/>
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</div>
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> **Notes:**
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> - End-to-End Inference Performance Comparison on OmniDocBench v1.5. PDF documents were processed in batches of 512 on a single NVIDIA A100 GPU. The reported end-to-end runtime includes both PDF rendering and Markdown generation. All methods rely on their built-in PDF parsing modules and default DPI settings to reflect out-of-the-box performance.
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## Visualization
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### Real-word Document Parsing
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#### Illumination
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/light.jpg" width="800"/>
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</div>
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#### Skew
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/skew.jpg" width="800"/>
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</div>
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#### Screen Photography
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/screen.jpg" width="800"/>
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</div>
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#### Scanning
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/scaning.jpg" width="800"/>
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</div>
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#### Warping
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/curving.jpg" width="800"/>
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</div>
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### Text Spotting
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/spotting.jpg" width="800"/>
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</div>
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### Seal Recognition
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<div align="center">
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<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/seal.jpg" width="800"/>
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</div>
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## Acknowledgments
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We would like to thank [PaddleFormers](https://github.com/PaddlePaddle/PaddleFormers), [Keye](https://github.com/Kwai-Keye/Keye), [MinerU](https://github.com/opendatalab/MinerU), [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project!
|
| 348 |
-
|
| 349 |
-
## Citation
|
| 350 |
-
|
| 351 |
-
If you find PaddleOCR-VL-1.5 helpful, feel free to give us a star and citation.
|
| 352 |
-
|
| 353 |
-
```bibtex
|
| 354 |
-
@misc{cui2026paddleocrvl15multitask09bvlm,
|
| 355 |
-
title={PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing},
|
| 356 |
-
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 Yi Liu and Dianhai Yu and Yanjun Ma},
|
| 357 |
-
year={2026},
|
| 358 |
-
eprint={2601.21957},
|
| 359 |
-
archivePrefix={arXiv},
|
| 360 |
-
primaryClass={cs.CV},
|
| 361 |
-
url={https://arxiv.org/abs/2601.21957},
|
| 362 |
-
}
|
| 363 |
-
```
|
|
|
|
| 1 |
---
|
| 2 |
+
license: other
|
| 3 |
+
library_name: transformers
|
| 4 |
tags:
|
| 5 |
+
- vox-jot
|
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|
| 6 |
- ocr
|
| 7 |
+
- mirror
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|
| 8 |
---
|
|
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|
| 9 |
|
| 10 |
+
# Vox Jot OCR Mirror: paddleocr-vl-1.5
|
| 11 |
|
| 12 |
+
This repository is an app-managed mirror for Vox Jot. It preserves upstream lineage so the desktop app can download OCR model files anonymously and reproducibly.
|
| 13 |
|
| 14 |
+
## Upstream Sources
|
| 15 |
|
| 16 |
+
- [PaddlePaddle/PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)
|
| 17 |
|
| 18 |
+
## License And Attribution
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|
| 19 |
|
| 20 |
+
- Upstream license: Apache-2.0
|
| 21 |
+
- Attribution: original model authors and upstream repositories listed above.
|
| 22 |
+
- Vox Jot changes: mirrored and repackaged for app-managed download; no model training changes are implied by this mirror.
|
| 23 |
+
- Notices: upstream README, LICENSE, NOTICE, and COPYING files found during staging are preserved under `upstream-notices/`.
|
| 24 |
|
| 25 |
+
## Notes
|
| 26 |
|
| 27 |
+
PaddleOCR-VL 1.5 (0.9B VLM).
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|
VOX_JOT_UPSTREAM.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
{
|
| 2 |
+
"catalog_id": "paddleocr-vl-1.5",
|
| 3 |
+
"mirror_repo": "IrieDinamik/ocr-mirror-paddleocr-vl-1.5",
|
| 4 |
+
"license_label": "Apache-2.0",
|
| 5 |
+
"upstream_sources": [
|
| 6 |
+
{
|
| 7 |
+
"type": "huggingface_model",
|
| 8 |
+
"id": "PaddlePaddle/PaddleOCR-VL-1.5",
|
| 9 |
+
"url": "https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5"
|
| 10 |
+
}
|
| 11 |
+
],
|
| 12 |
+
"notes": "PaddleOCR-VL 1.5 (0.9B VLM).",
|
| 13 |
+
"notice": "Vox Jot mirrors these OCR assets for app-managed installation. Preserve upstream LICENSE, NOTICE, COPYING, and README/model-card files when redistributing."
|
| 14 |
+
}
|
upstream-notices/PaddlePaddle__PaddleOCR-VL-1.5/LICENSE
ADDED
|
@@ -0,0 +1,201 @@
|
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|
| 1 |
+
Apache License
|
| 2 |
+
Version 2.0, January 2004
|
| 3 |
+
http://www.apache.org/licenses/
|
| 4 |
+
|
| 5 |
+
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
| 6 |
+
|
| 7 |
+
1. Definitions.
|
| 8 |
+
|
| 9 |
+
"License" shall mean the terms and conditions for use, reproduction,
|
| 10 |
+
and distribution as defined by Sections 1 through 9 of this document.
|
| 11 |
+
|
| 12 |
+
"Licensor" shall mean the copyright owner or entity authorized by
|
| 13 |
+
the copyright owner that is granting the License.
|
| 14 |
+
|
| 15 |
+
"Legal Entity" shall mean the union of the acting entity and all
|
| 16 |
+
other entities that control, are controlled by, or are under common
|
| 17 |
+
control with that entity. For the purposes of this definition,
|
| 18 |
+
"control" means (i) the power, direct or indirect, to cause the
|
| 19 |
+
direction or management of such entity, whether by contract or
|
| 20 |
+
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
| 21 |
+
outstanding shares, or (iii) beneficial ownership of such entity.
|
| 22 |
+
|
| 23 |
+
"You" (or "Your") shall mean an individual or Legal Entity
|
| 24 |
+
exercising permissions granted by this License.
|
| 25 |
+
|
| 26 |
+
"Source" form shall mean the preferred form for making modifications,
|
| 27 |
+
including but not limited to software source code, documentation
|
| 28 |
+
source, and configuration files.
|
| 29 |
+
|
| 30 |
+
"Object" form shall mean any form resulting from mechanical
|
| 31 |
+
transformation or translation of a Source form, including but
|
| 32 |
+
not limited to compiled object code, generated documentation,
|
| 33 |
+
and conversions to other media types.
|
| 34 |
+
|
| 35 |
+
"Work" shall mean the work of authorship, whether in Source or
|
| 36 |
+
Object form, made available under the License, as indicated by a
|
| 37 |
+
copyright notice that is included in or attached to the work
|
| 38 |
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(an example is provided in the Appendix below).
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| 39 |
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|
| 40 |
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"Derivative Works" shall mean any work, whether in Source or Object
|
| 41 |
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form, that is based on (or derived from) the Work and for which the
|
| 42 |
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editorial revisions, annotations, elaborations, or other modifications
|
| 43 |
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represent, as a whole, an original work of authorship. For the purposes
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| 44 |
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of this License, Derivative Works shall not include works that remain
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separable from, or merely link (or bind by name) to the interfaces of,
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| 48 |
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"Contribution" shall mean any work of authorship, including
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|
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subsequently incorporated within the Work.
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2. Grant of Copyright License. Subject to the terms and conditions of
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upstream-notices/PaddlePaddle__PaddleOCR-VL-1.5/README.md
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
pipeline_tag: image-text-to-text
|
| 4 |
+
tags:
|
| 5 |
+
- ERNIE4.5
|
| 6 |
+
- PaddleOCR
|
| 7 |
+
- PaddlePaddle
|
| 8 |
+
- image-to-text
|
| 9 |
+
- ocr
|
| 10 |
+
- document-parse
|
| 11 |
+
- layout
|
| 12 |
+
- table
|
| 13 |
+
- formula
|
| 14 |
+
- chart
|
| 15 |
+
- seal
|
| 16 |
+
- spotting
|
| 17 |
+
base_model: baidu/ERNIE-4.5-0.3B-Paddle
|
| 18 |
+
language:
|
| 19 |
+
- en
|
| 20 |
+
- zh
|
| 21 |
+
- multilingual
|
| 22 |
+
library_name: PaddleOCR
|
| 23 |
+
---
|
| 24 |
+
<div align="center">
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
<h1 align="center">
|
| 28 |
+
|
| 29 |
+
PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
|
| 30 |
+
|
| 31 |
+
</h1>
|
| 32 |
+
|
| 33 |
+
[](https://github.com/PaddlePaddle/PaddleOCR)
|
| 34 |
+
[](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)
|
| 35 |
+
[](https://modelscope.cn/models/PaddlePaddle/PaddleOCR-VL-1.5)
|
| 36 |
+
[](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo)
|
| 37 |
+
[](https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL-1.5_Online_Demo/summary)
|
| 38 |
+
[](https://discord.gg/JPmZXDsEEK)
|
| 39 |
+
[](https://x.com/PaddlePaddle)
|
| 40 |
+
[](./LICENSE)
|
| 41 |
+
|
| 42 |
+
**🔥 [Official Website](https://www.paddleocr.com)**
|
| 43 |
+
**📝 [Technical Report](https://arxiv.org/abs/2601.21957)**
|
| 44 |
+
|
| 45 |
+
</div>
|
| 46 |
+
|
| 47 |
+
<div align="center">
|
| 48 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/paddleocr-vl-1.5_metrics.png" width="800"/>
|
| 49 |
+
</div>
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
## Introduction
|
| 54 |
+
|
| 55 |
+
**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.
|
| 56 |
+
|
| 57 |
+
### **Key Capabilities of PaddleOCR-VL-1.5**
|
| 58 |
+
|
| 59 |
+
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.**
|
| 60 |
+
|
| 61 |
+
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.
|
| 62 |
+
|
| 63 |
+
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.
|
| 64 |
+
|
| 65 |
+
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.**
|
| 66 |
+
|
| 67 |
+
5. The model supports **automatic cross-page table merging** and **cross-page paragraph heading recognition**, effectively mitigating content fragmentation issues in **long-document parsing.**
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
### **Model Architecture**
|
| 71 |
+
|
| 72 |
+
<div align="center">
|
| 73 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/PaddleOCR-VL-1.5.png" width="800"/>
|
| 74 |
+
</div>
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
## News
|
| 78 |
+
|
| 79 |
+
* ```2026.03.06``` 🚀 Support llama.cpp inference for the VLM component in PaddleOCR-VL-1.5. Click [here](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5-GGUF) for details.
|
| 80 |
+
* ```2026.01.29``` 🚀 We release [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5), —a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.
|
| 81 |
+
|
| 82 |
+
## Usage
|
| 83 |
+
|
| 84 |
+
### Install Dependencies
|
| 85 |
+
|
| 86 |
+
Install [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR):
|
| 87 |
+
|
| 88 |
+
```bash
|
| 89 |
+
# 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
|
| 90 |
+
python -m pip install paddlepaddle-gpu==3.2.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
|
| 91 |
+
python -m pip install -U "paddleocr[doc-parser]"
|
| 92 |
+
```
|
| 93 |
+
|
| 94 |
+
> **Please ensure that you install PaddlePaddle framework version 3.2.1 or above, along with the special version of safetensors.** For macOS users, please use Docker to set up the environment.
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
### Basic Usage
|
| 98 |
+
|
| 99 |
+
CLI usage:
|
| 100 |
+
|
| 101 |
+
```bash
|
| 102 |
+
paddleocr doc_parser -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png
|
| 103 |
+
```
|
| 104 |
+
|
| 105 |
+
Python API usage:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from paddleocr import PaddleOCRVL
|
| 109 |
+
pipeline = PaddleOCRVL()
|
| 110 |
+
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
|
| 111 |
+
for res in output:
|
| 112 |
+
res.print()
|
| 113 |
+
res.save_to_json(save_path="output")
|
| 114 |
+
res.save_to_markdown(save_path="output")
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
### Accelerate VLM Inference via Optimized Inference Servers
|
| 118 |
+
|
| 119 |
+
1. Start the VLM inference server:
|
| 120 |
+
|
| 121 |
+
You can start the vLLM inference service using one of two methods:
|
| 122 |
+
- Method 1: PaddleOCR method
|
| 123 |
+
|
| 124 |
+
```bash
|
| 125 |
+
docker run \
|
| 126 |
+
--rm \
|
| 127 |
+
--gpus all \
|
| 128 |
+
--network host \
|
| 129 |
+
ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-genai-vllm-server:latest-nvidia-gpu \
|
| 130 |
+
paddleocr genai_server --model_name PaddleOCR-VL-1.5-0.9B --host 0.0.0.0 --port 8080 --backend vllm
|
| 131 |
+
```
|
| 132 |
+
- Method 2: vLLM method
|
| 133 |
+
|
| 134 |
+
[vLLM: PaddleOCR-VL Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/PaddlePaddle/PaddleOCR-VL.html)
|
| 135 |
+
|
| 136 |
+
2. Call the PaddleOCR CLI or Python API:
|
| 137 |
+
```bash
|
| 138 |
+
paddleocr doc_parser \
|
| 139 |
+
-i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png \
|
| 140 |
+
--vl_rec_backend vllm-server \
|
| 141 |
+
--vl_rec_server_url http://127.0.0.1:8080/v1
|
| 142 |
+
```
|
| 143 |
+
```python
|
| 144 |
+
from paddleocr import PaddleOCRVL
|
| 145 |
+
pipeline = PaddleOCRVL(vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8080/v1")
|
| 146 |
+
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
|
| 147 |
+
for res in output:
|
| 148 |
+
res.print()
|
| 149 |
+
res.save_to_json(save_path="output")
|
| 150 |
+
res.save_to_markdown(save_path="output")
|
| 151 |
+
```
|
| 152 |
+
|
| 153 |
+
**For more usage details and parameter explanations, see the [documentation](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html).**
|
| 154 |
+
## PaddleOCR-VL-1.5-0.9B Usage with transformers
|
| 155 |
+
|
| 156 |
+
Currently, the PaddleOCR-VL-1.5-0.9B model facilitates seamless inference via the `transformers` library, supporting **comprehensive text spotting** and the recognition of complex elements including formulas, tables, charts, and seals. Below is a simple script we provide to support inference using the PaddleOCR-VL-1.5-0.9B model with `transformers`.
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
> [!NOTE]
|
| 160 |
+
> Note: We currently recommend using the official method for inference, as it is faster and supports page-level document parsing. The example code below only supports element-level recognition and text spotting.
|
| 161 |
+
|
| 162 |
+
```shell
|
| 163 |
+
# ensure the transformers v5 is installed
|
| 164 |
+
python -m pip install "transformers>=5.0.0"
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
```python
|
| 168 |
+
from PIL import Image
|
| 169 |
+
import torch
|
| 170 |
+
from transformers import AutoProcessor, AutoModelForImageTextToText
|
| 171 |
+
|
| 172 |
+
# ---- Settings ----
|
| 173 |
+
model_path = "PaddlePaddle/PaddleOCR-VL-1.5"
|
| 174 |
+
image_path = "test.png"
|
| 175 |
+
task = "ocr" # Options: 'ocr' | 'table' | 'chart' | 'formula' | 'spotting' | 'seal'
|
| 176 |
+
# ------------------
|
| 177 |
+
|
| 178 |
+
# ---- Image Preprocessing For Spotting ----
|
| 179 |
+
image = Image.open(image_path).convert("RGB")
|
| 180 |
+
orig_w, orig_h = image.size
|
| 181 |
+
spotting_upscale_threshold = 1500
|
| 182 |
+
|
| 183 |
+
if task == "spotting" and orig_w < spotting_upscale_threshold and orig_h < spotting_upscale_threshold:
|
| 184 |
+
process_w, process_h = orig_w * 2, orig_h * 2
|
| 185 |
+
try:
|
| 186 |
+
resample_filter = Image.Resampling.LANCZOS
|
| 187 |
+
except AttributeError:
|
| 188 |
+
resample_filter = Image.LANCZOS
|
| 189 |
+
image = image.resize((process_w, process_h), resample_filter)
|
| 190 |
+
|
| 191 |
+
# Set max_pixels: use 1605632 for spotting, otherwise use default ~1M pixels
|
| 192 |
+
max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28
|
| 193 |
+
# ---------------------------
|
| 194 |
+
|
| 195 |
+
# -------- Inference --------
|
| 196 |
+
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
| 197 |
+
PROMPTS = {
|
| 198 |
+
"ocr": "OCR:",
|
| 199 |
+
"table": "Table Recognition:",
|
| 200 |
+
"formula": "Formula Recognition:",
|
| 201 |
+
"chart": "Chart Recognition:",
|
| 202 |
+
"spotting": "Spotting:",
|
| 203 |
+
"seal": "Seal Recognition:",
|
| 204 |
+
}
|
| 205 |
+
|
| 206 |
+
model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(DEVICE).eval()
|
| 207 |
+
processor = AutoProcessor.from_pretrained(model_path)
|
| 208 |
+
|
| 209 |
+
messages = [
|
| 210 |
+
{
|
| 211 |
+
"role": "user",
|
| 212 |
+
"content": [
|
| 213 |
+
{"type": "image", "image": image},
|
| 214 |
+
{"type": "text", "text": PROMPTS[task]},
|
| 215 |
+
]
|
| 216 |
+
}
|
| 217 |
+
]
|
| 218 |
+
inputs = processor.apply_chat_template(
|
| 219 |
+
messages,
|
| 220 |
+
add_generation_prompt=True,
|
| 221 |
+
tokenize=True,
|
| 222 |
+
return_dict=True,
|
| 223 |
+
return_tensors="pt",
|
| 224 |
+
images_kwargs={"size": {"shortest_edge": processor.image_processor.min_pixels, "longest_edge": max_pixels}},
|
| 225 |
+
).to(model.device)
|
| 226 |
+
|
| 227 |
+
outputs = model.generate(**inputs, max_new_tokens=512)
|
| 228 |
+
result = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:-1])
|
| 229 |
+
print(result)
|
| 230 |
+
# ---------------------------
|
| 231 |
+
```
|
| 232 |
+
|
| 233 |
+
<details>
|
| 234 |
+
<summary>👉 Click to expand: Use flash-attn to boost performance and reduce memory usage</summary>
|
| 235 |
+
|
| 236 |
+
```shell
|
| 237 |
+
# ensure the flash-attn2 is installed
|
| 238 |
+
pip install flash-attn --no-build-isolation
|
| 239 |
+
```
|
| 240 |
+
|
| 241 |
+
```python
|
| 242 |
+
model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").to(DEVICE).eval()
|
| 243 |
+
```
|
| 244 |
+
|
| 245 |
+
</details>
|
| 246 |
+
|
| 247 |
+
## Performance
|
| 248 |
+
|
| 249 |
+
### Document Parsing
|
| 250 |
+
|
| 251 |
+
|
| 252 |
+
#### 1. OmniDocBench v1.5
|
| 253 |
+
|
| 254 |
+
##### PaddleOCR-VL-1.5 achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5
|
| 255 |
+
|
| 256 |
+
<div align="center">
|
| 257 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/omnidocbenchv1.5_metrics.png" width="800"/>
|
| 258 |
+
</div>
|
| 259 |
+
|
| 260 |
+
|
| 261 |
+
> **Notes:**
|
| 262 |
+
> - Performance metrics are cited from the [OmniDocBench official leaderboard](https://opendatalab.com/omnidocbench), except for Gemini-3 Pro, Qwen3-VL-235B-A22B-Instruct and our model, which were evaluated independently.
|
| 263 |
+
|
| 264 |
+
|
| 265 |
+
#### 2. Real5-OmniDocBench
|
| 266 |
+
|
| 267 |
+
##### Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.5 consistently sets new SOTA records
|
| 268 |
+
|
| 269 |
+
|
| 270 |
+
<div align="center">
|
| 271 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/real5-omnidocbench_metrics.png" width="800"/>
|
| 272 |
+
</div>
|
| 273 |
+
|
| 274 |
+
> **Notes:**
|
| 275 |
+
> - Real5-OmniDocBench is a brand-new benchmark oriented toward real-world scenarios, which we constructed based on the OmniDocBench v1.5 dataset. The dataset comprises five distinct scenarios: Scanning, Warping, Screen-photography, Illumination, and Skew. For further details, please refer to [Real5-OmniDocBench](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench).
|
| 276 |
+
|
| 277 |
+
|
| 278 |
+
### Inference Performance
|
| 279 |
+
|
| 280 |
+
|
| 281 |
+
<div align="center">
|
| 282 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/inference_performance.png" width="800"/>
|
| 283 |
+
</div>
|
| 284 |
+
|
| 285 |
+
> **Notes:**
|
| 286 |
+
> - End-to-End Inference Performance Comparison on OmniDocBench v1.5. PDF documents were processed in batches of 512 on a single NVIDIA A100 GPU. The reported end-to-end runtime includes both PDF rendering and Markdown generation. All methods rely on their built-in PDF parsing modules and default DPI settings to reflect out-of-the-box performance.
|
| 287 |
+
|
| 288 |
+
|
| 289 |
+
## Visualization
|
| 290 |
+
|
| 291 |
+
|
| 292 |
+
### Real-word Document Parsing
|
| 293 |
+
|
| 294 |
+
|
| 295 |
+
#### Illumination
|
| 296 |
+
|
| 297 |
+
<div align="center">
|
| 298 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/light.jpg" width="800"/>
|
| 299 |
+
</div>
|
| 300 |
+
|
| 301 |
+
|
| 302 |
+
#### Skew
|
| 303 |
+
|
| 304 |
+
<div align="center">
|
| 305 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/skew.jpg" width="800"/>
|
| 306 |
+
</div>
|
| 307 |
+
|
| 308 |
+
|
| 309 |
+
#### Screen Photography
|
| 310 |
+
|
| 311 |
+
<div align="center">
|
| 312 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/screen.jpg" width="800"/>
|
| 313 |
+
</div>
|
| 314 |
+
|
| 315 |
+
|
| 316 |
+
#### Scanning
|
| 317 |
+
|
| 318 |
+
<div align="center">
|
| 319 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/scaning.jpg" width="800"/>
|
| 320 |
+
</div>
|
| 321 |
+
|
| 322 |
+
#### Warping
|
| 323 |
+
|
| 324 |
+
<div align="center">
|
| 325 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/curving.jpg" width="800"/>
|
| 326 |
+
</div>
|
| 327 |
+
|
| 328 |
+
|
| 329 |
+
### Text Spotting
|
| 330 |
+
|
| 331 |
+
|
| 332 |
+
<div align="center">
|
| 333 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/spotting.jpg" width="800"/>
|
| 334 |
+
</div>
|
| 335 |
+
|
| 336 |
+
|
| 337 |
+
### Seal Recognition
|
| 338 |
+
|
| 339 |
+
|
| 340 |
+
<div align="center">
|
| 341 |
+
<img src="https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/refs/heads/main/images/paddleocr_vl_1_5/seal.jpg" width="800"/>
|
| 342 |
+
</div>
|
| 343 |
+
|
| 344 |
+
|
| 345 |
+
## Acknowledgments
|
| 346 |
+
|
| 347 |
+
We would like to thank [PaddleFormers](https://github.com/PaddlePaddle/PaddleFormers), [Keye](https://github.com/Kwai-Keye/Keye), [MinerU](https://github.com/opendatalab/MinerU), [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project!
|
| 348 |
+
|
| 349 |
+
## Citation
|
| 350 |
+
|
| 351 |
+
If you find PaddleOCR-VL-1.5 helpful, feel free to give us a star and citation.
|
| 352 |
+
|
| 353 |
+
```bibtex
|
| 354 |
+
@misc{cui2026paddleocrvl15multitask09bvlm,
|
| 355 |
+
title={PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing},
|
| 356 |
+
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 Yi Liu and Dianhai Yu and Yanjun Ma},
|
| 357 |
+
year={2026},
|
| 358 |
+
eprint={2601.21957},
|
| 359 |
+
archivePrefix={arXiv},
|
| 360 |
+
primaryClass={cs.CV},
|
| 361 |
+
url={https://arxiv.org/abs/2601.21957},
|
| 362 |
+
}
|
| 363 |
+
```
|