ClusterTabNet: Supervised clustering method for table detection and table structure recognition

Marek Polewczyk, Marco Spinaci
2/12/2024
cs.LGcs.CV

No official code found. Best implementation: SAP-samples/clustertabnet (13 stars, Apache-2.0, last commit 2025-03-15).

Abstract

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.

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Code Implementations(1)

SAP-samples/clustertabnetCommunity100%
Apache-2.0

Implementation of the table detection and table structure recognition deep learning model described in the paper "ClusterTabNet: Supervised clustering method for table detection and table structure recognition".

131Python, Jupyter NotebookFeb 16, 20241 years agoApache-2.0
document-analysis-recognitionicdaricdar2024machine-learningresearch+1 more

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Cite this paper

@article{polewczyk2024clustertabnet,
  title  = {ClusterTabNet: Supervised clustering method for table detection and table structure recognition},
  author = {Marek Polewczyk and Marco Spinaci},
  year   = {2024},
  eprint = {2402.07502},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.07502v2}
}

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