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.
@article{arxiv.2402.07502,
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}
}
Comments
16 pages, 5 figures, accepted to ICDAR 2024. Code available at https://github.com/SAP-samples/clustertabnet