curl -X POST https://www.datalab.to/api/v1/{endpoint} \
  -H "X-API-Key: YOUR_API_KEY" \
  -F "file=@library/document.pdf"

{
  "success": true,
  "error": null,
  "request_id": "<string>",
  "request_check_url": "<string>"
}

curl -X GET https://www.datalab.to/api/v1/{engine_name}/{endpoint} \
  -H "X-API-Key: YOUR_API_KEY"

​

Authentication

​

Marker

import requests

url = "https://www.datalab.to/api/v1/marker"

form_data = {
    'file': ('test.pdf', open('~/pdfs/test.pdf', 'rb'), 'application/pdf'),
    "force_ocr": (None, False),
    "paginate": (None, False),
    'output_format': (None, 'markdown'),
    "use_llm": (None, False),
    "strip_existing_ocr": (None, False),
    "disable_image_extraction": (None, False)
}

headers = {"X-Api-Key": "YOUR_API_KEY"}

response = requests.post(url, files=form_data, headers=headers)
data = response.json()

• file, the input file.
• output_format - one of json, html, or markdown.
• force_ocr will force OCR on every page (ignore the text in the PDF). This is slower, but can be useful for PDFs with known bad text.
• format_lines will partially OCR the lines to properly include inline math and styling (bold, superscripts, etc.). This is faster than force_ocr.
• paginate - adds delimiters to the output pages. See the API reference for details.
• use_llm - setting this to True will use an LLM to enhance accuracy of forms, tables, inline math, and layout. It can be much more accurate, but carries a small hallucination risk. Setting use_llm to True will make responses slower.
• strip_existing_ocr - setting to True will remove all existing OCR text from the file and redo OCR. This is useful if you know OCR text was added to the PDF by a low-quality OCR tool.
• disable_image_extraction - setting to True will disable extraction of images. If use_llm is set to True, this will also turn images into text descriptions.
• max_pages - from the start of the file, specifies the maximum number of pages to inference.

{'success': True, 'error': None, 'request_id': "PpK1oM-HB4RgrhsQhVb2uQ", 'request_check_url': 'https://www.datalab.to/api/v1/marker/PpK1oM-HB4RgrhsQhVb2uQ'}

import time

max_polls = 300
check_url = data["request_check_url"]

for i in range(max_polls):
    time.sleep(2)
    response = requests.get(check_url, headers=headers) # Don't forget to send the auth headers
    data = response.json()

    if data["status"] == "complete":
        break

{
    "output_format": "markdown",
    "markdown": "...",
    "status": "complete",
    "success": True,
    "images": {...},
    "metadata": {...},
    "error": "",
    "page_count": 5
}

​

Response fields

• output_format is the requested output format, json, html, or markdown.
• markdown | json | html is the output from the file. It will be named according to the output_format. You can find more details on the json format here.
• status - indicates the status of the request (complete, or processing).
• success - indicates if the request completed successfully. True or False.
• images - dictionary of image filenames (keys) and base64 encoded images (values). Each value can be decoded with base64.b64decode(value). Then it can be saved to the filename (key).
• meta - metadata about the markdown conversion.
• error - if there was an error, this contains the error message.
• page_count - number of pages that were converted.

​

OCR

import requests

url = "https://www.datalab.to/api/v1/ocr"

form_data = {
    'file': ('test.png', open('~/images/test.png', 'rb'), 'image/png'),
}

headers = {"X-Api-Key": "YOUR_API_KEY"}

response = requests.post(url, files=form_data, headers=headers)
data = response.json()

{'success': True, 'error': None, 'request_id': "PpK1oM-HB4RgrhsQhVb2uQ", 'request_check_url': 'https://www.datalab.to/api/v1/ocr/PpK1oM-HB4RgrhsQhVb2uQ'}

{
    'status': 'complete',
    'pages': [
        {
            'text_lines': [{
                'polygon': [[267.0, 139.0], [525.0, 139.0], [525.0, 159.0], [267.0, 159.0]],
                'confidence': 0.99951171875,
                'text': 'Subspace Adversarial Training',
                'bbox': [267.0, 139.0, 525.0, 159.0]
            }, ...],
            'image_bbox': [0.0, 0.0, 816.0, 1056.0],
            'page': 12
        }
    ],
    'success': True,
    'error': '',
    'page_count': 5
}

​

Response fields

• status - indicates the status of the request (complete, or processing).
• success - indicates if the request completed successfully. True or False.
• error - If there was an error, this is the error message.
• page_count - number of pages we ran ocr on.
• pages - a list containing one dictionary per input page. The fields are:
  • text_lines - the detected text and bounding boxes for each line
    • text - the text in the line
    • confidence - the confidence of the model in the detected text (0-1)
    • polygon - the polygon for the text line in (x1, y1), (x2, y2), (x3, y3), (x4, y4) format. The points are in clockwise order from the top left.
    • bbox - the axis-aligned rectangle for the text line in (x1, y1, x2, y2) format. (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner.
    • chars - the individual characters in the line
      • text - the text of the character
      • bbox - the character bbox (same format as line bbox)
      • polygon - the character polygon (same format as line polygon)
      • confidence - the confidence of the model in the detected character (0-1)
      • bbox_valid - if the character is a special token or math, the bbox may not be valid
  • page - the page number in the file
  • image_bbox - the bbox for the image in (x1, y1, x2, y2) format. (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner. All line bboxes will be contained within this bbox.