TableNet-pytorch and pyramidtabnet
These are competitors—both are independent deep learning architectures (TableNet and PyramidTabNet) designed to solve the same table detection and recognition problem from document images, and a user would select one based on performance characteristics rather than using them together.
About TableNet-pytorch
asagar60/TableNet-pytorch
Pytorch Implementation of TableNet
This project helps you automatically extract structured data from tables found within images or scanned documents. It takes an image containing a table as input and outputs the identified table's location, its column structure, and the text content of each cell, making it easy to convert visual information into usable data. This is ideal for data entry specialists, researchers, or anyone needing to digitize information locked in image-based tables.
About pyramidtabnet
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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