muhd-umer/pyramidtabnet
Official PyTorch implementation of PyramidTabNet: Transformer-based Table Recognition in Image-based Documents
PyramidTabNet helps you automatically extract structured table data from scanned documents, images, and PDFs. It takes an image-based document containing tables as input and precisely identifies the tables and their internal structure (rows and columns). This is ideal for data entry specialists, researchers, and operations teams who need to convert visual table information into an editable, structured format for analysis or database entry.
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Use this if you need to accurately detect tables and understand their layout in various image-based documents, saving significant manual data entry time.
Not ideal if your documents are already in a structured, machine-readable format like Excel or CSV, or if you only need to extract plain text without table recognition.
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Language
Python
License
MIT
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Last pushed
Oct 05, 2024
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