SAP-samples/clustertabnet
Implementation of the table detection and table structure recognition deep learning model described in the paper "ClusterTabNet: Supervised clustering method for table detection and table structure recognition".
Extracts tables and their internal structure from images or scanned documents. It takes an image containing tables as input and outputs the detected tables along with their rows, columns, and individual cells. This is ideal for data analysts, researchers, or anyone who needs to digitize information trapped in document images.
No commits in the last 6 months.
Use this if you need to automatically locate tables within documents and understand their cell-level structure for data extraction or analysis.
Not ideal if you are working with born-digital, structured data like CSVs or databases, as this tool is specifically for extracting information from images.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Mar 15, 2025
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