Table Recognition - Datalab Documentation

Deprecated: The standalone Table Recognition endpoint (/api/v1/table_rec) is deprecated. Table extraction is now integrated into the Convert API.Use the Convert API with output_format: "json" to get structured table data with bounding boxes.

Recommended Approach

Use the Convert API for table extraction:

from datalab_sdk import DatalabClient, ConvertOptions

client = DatalabClient()

options = ConvertOptions(
    output_format="json",
    mode="balanced"
)

result = client.convert("document.pdf", options=options)

# Tables are in the JSON output with block_type: "Table"
for block in result.json.get("children", []):
    if block.get("block_type") == "Table":
        print(f"Table found: {block['id']}")
        print(f"Bounding box: {block['bbox']}")
        # Access table cells in block['children']

REST API

curl -X POST https://www.datalab.to/api/v1/convert \
  -H "X-API-Key: YOUR_API_KEY" \
  -F "file=@document.pdf" \
  -F "output_format=json" \
  -F "mode=balanced"

The JSON response includes Table and TableCell blocks with bounding boxes.

Why Use Marker Instead?

Single endpoint - No need for a separate table-specific call
Better integration - Tables are extracted in context with the full document
More features - Access processing modes, structured extraction, and more
Consistent API - Same patterns as all other document processing

Document Conversion - Full Convert API documentation
Structured Extraction - Extract specific data from tables

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