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End-to-End Semi-Supervised approach with Modulated Object Queries for Table Detection in Documents
May 9, 2024, 4:45 a.m. | Iqraa Ehsan, Tahira Shehzadi, Didier Stricker, Muhammad Zeshan Afzal
cs.CV updates on arXiv.org arxiv.org
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 cs.cv 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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