ClusterTabNet: Supervised clustering method for table detection and
  table structure recognition
Marek Polewczyk Marco Spinaci
We present a novel deep-learning-based method to cluster words in documents
which we apply to detect and recognize tables given the OCR output. We
interpret table structure bottom-up as a graph of relations between pairs of
words (belonging to the same row, column, header, as well as to the same table)
and use a transformer encoder model to predict its adjacency matrix. We
demonstrate the performance of our method on the PubTables-1M dataset as well
as PubTabNet and FinTabNet datasets. Compared to the current state-of-the-art
detection methods such as DETR and Faster R-CNN, our method achieves similar or
better accuracy, while requiring a significantly smaller model.