PaddleOCR-VL Service Deployment & API Usage Example:

PaddleOCR open-source project GitHub address, this service is built based on the PaddleOCR-VL model from this open-source project.

Version Information: The current version on the PaddleOCR official website corresponds to PaddleX version 3.4.0 and PaddlePaddle version 3.2.1.

1. Introduction to PaddleOCR-VL

On January 29, 2026, we released PaddleOCR-VL-1.5. PaddleOCR-VL-1.5 not only significantly improved the accuracy on the OmniDocBench v1.5 evaluation set to 94.5%, but also innovatively supports irregular-shaped bounding box localization. As a result, PaddleOCR-VL-1.5 demonstrates outstanding performance in real-world scenarios such as Skew, Warping, Screen Photography, Illumination, and Scanning. In addition, the model has added new capabilities for seal (stamp) recognition and text detection and recognition, with key metrics continuing to lead the industry.

Key Metrics:

The following diagram illustrates the overall workflow of PaddleOCR-VL-1.5 and its newly added capabilities:

2. API Quota Rules and Error Code Description

Please refer to the documentation.

3. Service Call Example (python)

# Please make sure the requests library is installed
# pip install requests
import base64
import os
import requests

# Please visit https://aistudio.baidu.com/paddleocr/task to obtain the API_URL and TOKEN in the API call example.
API_URL = "<your url>"
TOKEN = "<access token>"

file_path = "<local file path>"

with open(file_path, "rb") as file:
    file_bytes = file.read()
    file_data = base64.b64encode(file_bytes).decode("ascii")

headers = {
    "Authorization": f"token {TOKEN}",
    "Content-Type": "application/json"
}

required_payload = {
    "file": file_data,
    "fileType": <file type>,  # For PDF documents, set `fileType` to 0; for images, set `fileType` to 1
}

optional_payload = {
    "useDocOrientationClassify": False,
    "useDocUnwarping": False,
    "useChartRecognition": False,
}

payload = {**required_payload, **optional_payload}


response = requests.post(API_URL, json=payload, headers=headers)
print(response.status_code)
assert response.status_code == 200
result = response.json()["result"]

output_dir = "output"
os.makedirs(output_dir, exist_ok=True)

for i, res in enumerate(result["layoutParsingResults"]):
    md_filename = os.path.join(output_dir, f"doc_{i}.md")
    with open(md_filename, "w", encoding="utf-8") as md_file:
        md_file.write(res["markdown"]["text"])
    print(f"Markdown document saved at {md_filename}")
    for img_path, img in res["markdown"]["images"].items():
        full_img_path = os.path.join(output_dir, img_path)
        os.makedirs(os.path.dirname(full_img_path), exist_ok=True)
        img_bytes = requests.get(img).content
        with open(full_img_path, "wb") as img_file:
            img_file.write(img_bytes)
        print(f"Image saved to: {full_img_path}")
    for img_name, img in res["outputImages"].items():
        img_response = requests.get(img)
        if img_response.status_code == 200:
            # Save image to local
            filename = os.path.join(output_dir, f"{img_name}_{i}.jpg")
            with open(filename, "wb") as f:
                f.write(img_response.content)
            print(f"Image saved to: {filename}")
        else:
            print(f"Failed to download image, status code: {img_response.status_code}")

Main operations provided by the service:

• The HTTP request method is POST.
• Both the request body and response body are in JSON format (JSON object).
• When the request is processed successfully, the response status code is 200, and the response body contains the following properties:

• When the request is not processed successfully, the response body contains the following properties:

Main operations provided by the service are as follows:

• infer

Performs document analysis.

POST /layout-parsing

4. Request Parameter Description

Serving:
  extra:
    max_num_input_imgs: null

Serving:
  visualize: False

• When the request is processed successfully, the result field in the response body has the following properties:

Each element in layoutParsingResults is an object with the following properties:

markdown is an object with the following properties:

• restructurePages

Restructure multi-page results (Optional).

POST /restructure-pages

• The properties of the request body are as follows:

Each element in pages is an object with the following properties:

• When the request is processed successfully, the result field in the response body has the following properties:

For details on the returned data structure and field descriptions, please refer to the documentation.

Note: If you encounter any issues during use, please feel free to submit feedback in the issue section.

PaddleOCR-VL-1.6 Service Deployment & API Usage Example:

PaddleOCR open-source project GitHub address, this service is built based on the PaddleOCR-VL-1.6 model from this open-source project.

Version Information: The current version on the PaddleOCR official website corresponds to PaddleX version 3.7.0 and PaddlePaddle version 3.2.1.

1. Introduction to PaddleOCR-VL-1.6

PaddleOCR-VL-1.6 further optimizes PaddleOCR-VL-1.5 by systematically analyzing under-optimized areas in the current model, applying targeted data optimization, and adopting refined post-training strategies. It achieves a new state-of-the-art (SOTA) result of 96.33% on the OmniDocBench v1.6 document parsing benchmark. PaddleOCR-VL-1.6 also reaches SOTA performance across all scenarios on Real5-OmniDocBench, a benchmark designed to evaluate robustness against real-world physical distortions. In addition, PaddleOCR-VL-1.6 outperforms PaddleOCR-VL-1.5 on three subtasks: seal recognition, text detection and recognition, and chart recognition, while still maintaining an ultra-compact 0.9B-parameter VLM and high efficiency.

Key Metrics:

Core Features:

1. SOTA performance in document parsing: With only 0.9B parameters, PaddleOCR-VL-1.6 achieves 96.33% accuracy on OmniDocBench v1.6, surpassing the previous SOTA model, PaddleOCR-VL-1.5. Significant improvements are observed in table, formula, and text recognition.
2. SOTA performance for document parsing across five real-world scenarios: PaddleOCR-VL-1.6 offers stronger robustness and practicality in real-world use cases. In evaluations across five real-world distortion scenarios—scanning, warping, skew, screen photography, and illumination variation—it outperforms mainstream open-source and closed-source models.
3. Enhanced multi-element recognition capabilities: Beyond improved layout parsing, PaddleOCR-VL-1.6 substantially strengthens recognition of complex tables, ancient books, and rare Chinese characters, while further improving three existing capabilities: chart parsing, seal recognition, and text detection and recognition.
4. Compact 0.9B architecture: PaddleOCR-VL-1.6 follows the compact 0.9B architecture of the PaddleOCR-VL series, enabling zero-cost adaptation and drop-in replacement.