Review
Version 1
Preserved in Portico This version is not peer-reviewed
A Survey of Graphical Page Object Detection with Deep Neural Networks
Version 1
: Received: 25 April 2021 / Approved: 28 April 2021 / Online: 28 April 2021 (10:17:49 CEST)
A peer-reviewed article of this Preprint also exists.
Bhatt, J.; Hashmi, K.A.; Afzal, M.Z.; Stricker, D. A Survey of Graphical Page Object Detection with Deep Neural Networks. Appl. Sci. 2021, 11, 5344. Bhatt, J.; Hashmi, K.A.; Afzal, M.Z.; Stricker, D. A Survey of Graphical Page Object Detection with Deep Neural Networks. Appl. Sci. 2021, 11, 5344.
Abstract
In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.
Keywords
Deep neural network; survey; document images; review paper; deep learning; performance evaluation; page object detection, graphical page objects; document image analysis; page segmentation
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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