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Cascade Network with Deformable Composite Backbone for Formula Detection in Scanned Document Images
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- Hashmi, Khurram Azeem (författare)
- Department of Computer Science, Technical University, 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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- Pagani, Alain (författare)
- 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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- Stricker, Didier (författare)
- Department of Computer Science, Technical University, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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- Afzal, Muhammad Zeshan (författare)
- Department of Computer Science, Technical University, 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)
- 2021-08-19
- 2021
- Engelska.
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 11:16
- Relaterad länk:
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https://doi.org/10.3...
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https://ltu.diva-por... (primary) (Raw object)
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- formula detection
- Cascade Mask R-CNN
- mathematical expression detection
- document image analysis
- deep neural networks
- computer vision
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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