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

• URL: http://arxiv.org/abs/2402.07502v2
• Date: Thu, 23 May 2024 11:16:53 GMT
• Title: ClusterTabNet: Supervised clustering method for table detection and table structure recognition
• Authors: Marek Polewczyk, Marco Spinaci,
• Abstract summary: 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 and use a transformer encoder model to predict its adjacency matrix. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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