ClusterTabNet: Supervised clustering method for table detection and table structure recognition
<|reference_start|>ClusterTabNet: Supervised clustering method for table detection and table structure recognition: 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.<|reference_end|>
@article{polewczyk2024clustertabnet:,
    title={ClusterTabNet: Supervised clustering method for table detection and
  table structure recognition},
    author={Marek Polewczyk and Marco Spinaci},
    journal={arXiv preprint arXiv:2402.07502},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07502},
    primaryClass={cs.LG cs.CV}
}