Search: WFRF:(Hashmi Khurram Azeem) >
Guided Table Struct...
Guided Table Structure Recognition through Anchor Optimization
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- Hashmi, Khurram Azeem (author)
- 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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- Stricker, Didier (author)
- search Center for Artificial Intelligence, 67663 Kaiserslautern, Germany; Department of Computer Science, University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Liwicki, Marcus (author)
- Luleå tekniska universitet,EISLAB
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- Afzal, Muhammad Noman (author)
- Bilojix Soft Technologies, Bahawalpur, Pakistan
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- Afzal, Muhammad Zeshan (author)
- 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
- English.
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In: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 113521-113534
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Abstract
Subject headings
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- This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art systems for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors for table structure recognition. Subsequently, these anchors are exploited to locate the rows and columns in tabular images. Furthermore, the paper introduces a simple and effective method that improves the results using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the two publicly available datasets of table structure recognition: ICDAR-2013 and TabStructDB. Moreover, we empirically established the validity of our method by implementing it on the previous approaches. We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F-measure of 94.19% (92.06% for rows and 96.32% for columns). Thus, a relative error reduction of more than 25% is achieved. Furthermore, our proposed post-processing improves the average F-measure to 95.46% that results in a relative error reduction of more than 35%. Moreover, we surpassed the baseline results on the TabStructDB dataset with an average F-measure of 94.57% (94.08% for rows and 95.06% for columns).
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Deep Neural Network
- Mask R-CNN
- Document Images
- Object Detection
- Table Structure Recognition
- Table Structure Extraction
- Table Understanding
- Maskininlärning
- Machine Learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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