Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Bhatt, J.; Hashmi, K.A.; Afzal, M.Z.; Stricker, D. A Survey of Graphical Page Object Detection with Deep Neural Networks. Preprints 2021, 2021040739 (doi: 10.20944/preprints202104.0739.v1). Bhatt, J.; Hashmi, K.A.; Afzal, M.Z.; Stricker, D. A Survey of Graphical Page Object Detection with Deep Neural Networks. Preprints 2021, 2021040739 (doi: 10.20944/preprints202104.0739.v1).

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.

Subject Areas

Deep neural network; survey; document images; review paper; deep learning; performance evaluation; page object detection, graphical page objects; document image analysis; page segmentation

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