PaddlePaddle/PaddleOCR-VL-1.6

Overview

PaddleOCR-VL-1.6 upgrades PaddleOCR-VL-1.5 with a region-aware data optimization framework (targeted enhancement of weak regions found in the previous model) and a progressive post-training recipe based on curated data selection and reinforcement learning:

• 96.33% on OmniDocBench v1.6 (new SOTA), plus new records on OmniDocBench v1.5 and Real5-OmniDocBench (scanning, warping, screen-photography, illumination, skew)
• Major gains in table, Chinese ancient document, and Chinese rare character recognition
• Improved seal/stamp recognition, text spotting, and chart recognition

The architecture is fully compatible with PaddleOCR-VL-1.5 (same 0.9B NaViT-style dynamic-resolution vision encoder + ERNIE-4.5-0.3B LM), so migration is plug-and-play: the vLLM launch command and feature flags are unchanged.

Prerequisites

• Hardware: 1x GPU (small VRAM footprint)
• vLLM >= 0.11.1

Install vLLM

uv venv
source .venv/bin/activate
uv pip install -U vllm

Launch command

vllm serve PaddlePaddle/PaddleOCR-VL-1.6 \
  --trust-remote-code \
  --max-num-batched-tokens 16384 \
  --no-enable-prefix-caching \
  --mm-processor-cache-gb 0

Tip: OCR workloads don't benefit much from prefix caching or image reuse, so disable those to avoid hashing/caching overhead.

Client Usage

Task-specific prompts (same task set as 1.5, including spotting and seal):

from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600)

TASKS = {
    "ocr": "OCR:",
    "table": "Table Recognition:",
    "formula": "Formula Recognition:",
    "chart": "Chart Recognition:",
    "spotting": "Spotting:",
    "seal": "Seal Recognition:",
}

response = client.chat.completions.create(
    model="PaddlePaddle/PaddleOCR-VL-1.6",
    messages=[{
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": "https://.../receipt.png"}},
            {"type": "text", "text": TASKS["spotting"]},
        ],
    }],
    temperature=0.0,
)
print(response.choices[0].message.content)

Offline Inference with PP-DocLayoutV3

Use separate venvs for vllm and paddlepaddle to avoid conflicts. The 1.6 pipeline requires paddleocr[doc-parser] >= 3.6.0 and pipeline_version="v1.6", which automatically uses PP-DocLayoutV3 for layout detection (PP-DocLayoutV2 belongs to the v1 pipeline). Pass vl_rec_api_model_name="PaddlePaddle/PaddleOCR-VL-1.6" so the client matches the model name the vLLM server exposes — otherwise it defaults to PaddleOCR-VL-1.6-0.9B and fails with "The model PaddleOCR-VL-1.6-0.9B does not exist."

uv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
uv pip install -U "paddleocr[doc-parser]>=3.6.0"
uv pip install safetensors

from paddleocr import PaddleOCRVL

pipeline = PaddleOCRVL(
    pipeline_version="v1.6",
    vl_rec_backend="vllm-server",
    vl_rec_server_url="http://localhost:8000/v1",
    vl_rec_api_model_name="PaddlePaddle/PaddleOCR-VL-1.6",
)

output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
for i, res in enumerate(output):
    res.save_to_json(save_path=f"output_{i}.json")
    res.save_to_markdown(save_path=f"output_{i}.md")

References

• PaddleOCR-VL-1.6 on Hugging Face
• Technical report (arXiv)
• PaddleOCR GitHub
• vLLM support PR