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

Autor:	Polewczyk, Marek, Spinaci, Marco
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 5 figures, accepted to ICDAR 2024. Code available at https://github.com/SAP-samples/clustertabnet
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.07502 Zobrazit plný text záznamu View this record from Arxiv