research-article

Spatial Pyramid Block for Oracle Bone Inscription Detection

Authors:

Jing XiongAuthors Info & Claims

ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

Pages 133 - 140

https://doi.org/10.1145/3384544.3384561

Published: 17 April 2020 Publication History

Abstract

The detection of Oracle Bone Inscription (OBI) is one of the most fundamental aspects of oracle bone morphology. However, the detection method depending on experts' experience requires longterm learning and accumulation for professional knowledge. This paper investigated the performance of the deep-learning-based object detection framework in the OBI dataset, then selected the one with the best performance as the baseline and made a series of optimization. Specifically, we first redesigned the sizes and ratios of the anchor box according to the data characteristics by using K- means clustering. Secondly, we extracted some typical noises from OBI for data augmentation. Finally, Focal Loss and Mixed-precision are used to improve the model precision and compress the memory footprint. To further improve the performance, the Spatial Pyramid Block is proposed, which can stabilize features and suppress noise interference. Experiments on our OBI benchmarks validate the superiority of the proposed method that achieves 82.1% F-measure suppressing several mainstream object detectors. Our dataset and algorithms will soon be available at http://jgw.aynu.edu.cn.

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Index Terms

Spatial Pyramid Block for Oracle Bone Inscription Detection
1. Software and its engineering
  1. Software creation and management
    1. Designing software

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ICSCA '20: Proceedings of the 2020 9th International Conference on Software and Computer Applications

February 2020

382 pages

ISBN:9781450376655

DOI:10.1145/3384544

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 April 2020

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National Natural Science Foundation of China

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ICSCA 2020

ICSCA 2020: 2020 9th International Conference on Software and Computer Applications

February 18 - 21, 2020

Langkawi, Malaysia

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Cited By

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https://doi.org/10.1007/978-981-97-8705-0_14
Wan XLi ZLiang DPan SFang Y(2024)OBCTeacherInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10386461:6Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103864
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https://dl.acm.org/doi/10.1007/s10032-024-00463-0
Gao FChen XLi BLiu YJiang RHan Y(2024)Linking unknown characters via oracle bone inscriptions retrievalMultimedia Systems10.1007/s00530-024-01327-730:3Online publication date: 15-Apr-2024
https://doi.org/10.1007/s00530-024-01327-7
Li ZLi XQian KFang Y(2024)ROBC: A Radical-Level Oracle Bone Character DatasetPattern Recognition and Computer Vision10.1007/978-981-97-8511-7_8(100-113)Online publication date: 18-Oct-2024
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Yue XLi HFujikawa YMeng L(2022)Dynamic Dataset Augmentation for Deep Learning-based Oracle Bone Inscriptions RecognitionJournal on Computing and Cultural Heritage 10.1145/353286815:4(1-20)Online publication date: 6-Dec-2022
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