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A Survey of Graphical Page Object Detection with Deep Neural Networks

Applied sciences, 2021-06, Vol.11 (12), p.5344 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app11125344

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  • Title:
    A Survey of Graphical Page Object Detection with Deep Neural Networks
  • Author: Bhatt, Jwalin ; Hashmi, Khurram Azeem ; Afzal, Muhammad Zeshan ; Stricker, Didier
  • Subjects: Algorithms ; Artificial neural networks ; Datasets ; Deep learning ; deep neural network ; document images ; Information processing ; Machine learning ; Methods ; Neural networks ; Object recognition ; page object detection ; performance evaluation ; review paper
  • Is Part Of: Applied sciences, 2021-06, Vol.11 (12), p.5344
  • Description: 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 make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app11125344
  • Source: Open Access: DOAJ Directory of Open Access Journals
    AUTh Library subscriptions: ProQuest Central
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