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Current Status and ...
Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks
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- Hashmi, Khurram Azeem (författare)
- German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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- Stricker, Didier (författare)
- German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Afzal, Muhammad Adnan (författare)
- Bilojix Soft Technologies, Bahawalpur, Pakistan
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- Afzal, Muhammad Ahtsham (författare)
- Bilojix Soft Technologies, Bahawalpur, Pakistan
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- Afzal, Muhammad Zeshan (författare)
- German Research Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgrage, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 87663-87685
- Relaterad länk:
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https://doi.org/10.1...
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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
- The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled the deep neural networks to outperform traditional state-of-the-art machine learning methods. Table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. Moreover, it presents a comprehensive understanding of the current state-of-the-art and related challenges of table understanding in document images. The leading datasets and their intricacies have been elaborated along with the quantitative results. Furthermore, a brief overview is given regarding the promising directions that can further improve table analysis in document images.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Deep neural network
- document images
- deep learning
- performance evaluation
- table recognition
- table detection
- table structure recognition
- table analysis
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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