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Towards End-to-End ...
Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer
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- Shehzadi, Tahira (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663, Kaiserslautern, Germany
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- Azeem Hashmi, Khurram (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663, Kaiserslautern, Germany
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- Stricker, Didier (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663, Kaiserslautern, Germany
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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- Zeshan Afzal, Muhammad (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; Mindgarage, Technical University of Kaiserslautern, 67663, Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663, Kaiserslautern, Germany
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(creator_code:org_t)
- Springer, 2023
- 2023
- Engelska.
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Ingår i: Document Analysis and Recognition - ICDAR 2023, Part II. - : Springer. ; , s. 51-76
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success in table detection. However, a significant amount of labeled data is required to train these models effectively. Many semi-supervised approaches are introduced to mitigate the need for a substantial amount of label data. These approaches use CNN-based detectors that rely on anchor proposals and post-processing stages such as NMS. To tackle these limitations, this paper presents a novel end-to-end semi-supervised table detection method that employs the deformable transformer for detecting table objects. We evaluate our semi-supervised method on PubLayNet, DocBank, ICADR-19 and TableBank datasets, and it achieves superior performance compared to previous methods. It outperforms the fully supervised method (Deformable transformer) by +3.4 points on 10% labels of TableBank-both dataset and the previous CNN-based semi-supervised approach (Soft Teacher) by +1.8 points on 10% labels of PubLayNet dataset. We hope this work opens new possibilities towards semi-supervised and unsupervised table detection methods.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Deformable Transformer
- Semi-Supervised Learning
- Table Analysis
- Table Detection
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)