PaddleOCR VL 1.6

PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training

🔥 Official Website

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. 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 and 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"

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

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.

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.

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

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

Acknowledgments

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

Citation

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