May 9, 2024, 4:45 a.m. | Iqraa Ehsan, Tahira Shehzadi, Didier Stricker, Muhammad Zeshan Afzal

cs.CV updates on

arXiv:2405.04971v1 Announce Type: new
Abstract: Table detection, a pivotal task in document analysis, aims to precisely recognize and locate tables within document images. Although deep learning has shown remarkable progress in this realm, it typically requires an extensive dataset of labeled data for proficient training. Current CNN-based semi-supervised table detection approaches use the anchor generation process and Non-Maximum Suppression (NMS) in their detection process, limiting training efficiency. Meanwhile, transformer-based semi-supervised techniques adopted a one-to-one match strategy that provides noisy pseudo-labels, …

abstract analysis arxiv cnn current data dataset deep learning detection document documents images object pivotal progress queries realm semi semi-supervised table table detection tables training type

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