# PaddleOCR VL 1.6 URL: https://interfaze.ai/models/paddlepaddlepaddleocr-vl-16 PaddleOCR VL 1.6 by PaddlePaddle, a image-text-to-text model with OCR, object detection, multimodal capabilities. Understand and compare OCR, object detection, multimodal features, benchmarks, and capabilities. ## Comparison | Feature | PaddleOCR VL 1.6 | Interfaze | | --- | --- | --- | | Input Modalities | image, text | image, text, audio, video, document | | Native OCR | Yes | Yes | | Long Document Processing | No | Yes | | Language Support | 109 partial | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | Yes | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 131.1K | 1M | | Tool Calling | No | Tool calling supported + built in browser, code execution and web search | ### OCR Capabilities | Feature | PaddleOCR VL 1.6 | Interfaze | | --- | --- | --- | | Text Bounding Boxes | Partial | Yes | | Confidence Scores | Partial | Yes | | Dense Image Processing | Yes | Yes | | Low Quality Images | Partial | Yes | | Handwritten Text | Partial | Yes | | Charts, Tables & Equations | Partial | Yes | ### Object Detection Capabilities | Feature | PaddleOCR VL 1.6 | Interfaze | | --- | --- | --- | | Object Bounding Boxes | Partial | Yes | | Object Segmentation Masks | No | Yes | | Confidence Scores | No | Yes | | Dense Image Processing | Yes | Yes | | Low Quality Images | Partial | Yes | | Industry-Specific | Yes | Yes | | GUI Element Detection | No | Yes | ### Scaling | Feature | PaddleOCR VL 1.6 | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6) PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training [![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) 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[![Discord](https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white)](https://discord.gg/JPmZXDsEEK) [![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) [![License](https://img.shields.io/badge/license-Apache_2.0-green)](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6/blob/main/LICENSE) **🔥 [Official Website](https://www.paddleocr.com)** ## Introduction We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to those regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. **PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well**, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration. ### **Key Capabilities of PaddleOCR-VL-1.6** **🚀 New SOTA Accuracy**: OmniDocBench v1.6 achieves **96.33%**, setting new state-of-the-art records on OmniDocBench v1.5 and Real5-OmniDocBench as well. It delivers comprehensive leading performance across text, formula, and table recognition, surpassing both open-source and closed-source solutions. **⚡ Fully Upgraded Capabilities**: Significant improvements in table, Chinese ancient document, and Chinese rare character recognition, along with notable enhancements in seal/stamp recognition, text spotting, chart recognition, and more diverse scenarios. **🔄 Seamless Migration**: The model architecture is **fully compatible with PaddleOCR-VL-1.5** — zero adaptation cost, plug-and-play replacement. ### **PaddleOCR-VL-1.6 Architecture** ### **PaddleOCR-VL-1.6 Data Engine** ## News - `2026.05.28` 🚀 We release [PaddleOCR-VL-1.6](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.6). PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration. ### Install Dependencies Install [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR): ``` 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]>=3.6.0" ``` > **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. ### 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 v1.6 ``` Python API usage: ``` from paddleocr import PaddleOCRVL pipeline = PaddleOCRVL(pipeline_version="v1.6") 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-1.6-0.9B --host 0.0.0.0 --port 8080 --backend vllm ``` - Method 2: vLLM method [vLLM: PaddleOCR-VL Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/PaddlePaddle/PaddleOCR-VL.html) 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 v1.6 \ --vl_rec_backend vllm-server \ --vl_rec_server_url http://127.0.0.1:8080/v1 ``` ``` from paddleocr import PaddleOCRVL pipeline = PaddleOCRVL(pipeline_version="v1.6", 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](https://www.paddleocr.ai/latest/en/version3.x/pipeline_usage/PaddleOCR-VL.html).** ## PaddleOCR-VL-1.6-0.9B Usage with transformers Currently, the PaddleOCR-VL-1.6-0.9B 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`. > \[!NOTE\] 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. ``` python -m pip install "transformers>=5.0.0" ``` ``` from PIL import Image import torch from transformers import AutoProcessor, AutoModelForImageTextToText model_path = "PaddlePaddle/PaddleOCR-VL-1.6" image_path = "test.png" task = "ocr" # Options: 'ocr' | 'table' | 'chart' | 'formula' | 'spotting' | 'seal' image = Image.open(image_path).convert("RGB") orig_w, orig_h = image.size spotting_upscale_threshold = 1500 if task == "spotting" and orig_w < spotting_upscale_threshold and orig_h < spotting_upscale_threshold: process_w, process_h = orig_w * 2, orig_h * 2 try: resample_filter = Image.Resampling.LANCZOS except AttributeError: resample_filter = Image.LANCZOS image = image.resize((process_w, process_h), resample_filter) max_pixels = 2048 * 28 * 28 if task == "spotting" else 1280 * 28 * 28 DEVICE = "cuda" if torch.cuda.is_available() else "cpu" PROMPTS = { "ocr": "OCR:", "table": "Table Recognition:", "formula": "Formula Recognition:", "chart": "Chart Recognition:", "spotting": "Spotting:", "seal": "Seal Recognition:", } model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(DEVICE).eval() processor = AutoProcessor.from_pretrained(model_path) messages = [ { "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": PROMPTS[task]}, ] } ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", images_kwargs={"size": {"shortest_edge": processor.image_processor.min_pixels, "longest_edge": max_pixels}}, ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) result = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:-1]) print(result) ``` ``` pip install flash-attn --no-build-isolation ``` ``` model = AutoModelForImageTextToText.from_pretrained(model_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").to(DEVICE).eval() ``` ## Performance ### Document Parsing #### 1\. OmniDocBench v1.6 ##### PaddleOCR-VL-1.6 achieves SOTA performance for overall, text, formula, tables on OmniDocBench v1.6 > **Notes:** > > - 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. #### 2\. Real5-OmniDocBench ##### Across all five diverse and challenging scenarios—scanning, warping, screen-photography, illumination, and skew—PaddleOCR-VL-1.6 consistently sets new SOTA records > **Notes:** > > - 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). ## Acknowledgments 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! ## Citation If you find PaddleOCR-VL-1.6 helpful, feel free to give us a star and citation. ``` comming soon ``` ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)