Image classification for historical documents: a study on Chinese local gazetteers.
We present a novel approach for automatically classifying illustrations from historical Chinese local gazetteers using modern deep learning techniques. Our goal is to facilitate the digital organization and study of a large quantity of digitized local gazetteers. We evaluate the performance of eight...
| Publicado en: | Digital Scholarship in the Humanities Vol. 39; no. 1; pp. 61 - 74 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Apr2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176806326&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176806326 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2024 vid: 39 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 176806326 10.1093/llc/fqad065 ppf: 61 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Image classification for historical documents: a study on Chinese local gazetteers. aug: au: Chen, Jhe-An Hou, Jen-Chien Tsai, Richard Tzong-Han Liao, Hsiung-Ming Chen, Shih-Pei Chang, Ming-Ching affil: Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica , Taipei 115201, Taiwan Computer Science and Information Engineering Department, National Central University , Taoyuan 320317, Taiwan Max Planck Institute for the History of Science , Berlin 14195, Germany Computer Science Department, University at Albany, State University of New York , Albany, NY 12222, USA su: Image recognition (Computer vision) Artificial neural networks Historical source material Deep learning Art materials Digital humanities sug: subj: Image recognition (Computer vision) Artificial neural networks Historical source material Deep learning Art materials Digital humanities keyword: art Chinese local gazetteers Convoluational Neural Network DaViT digital humanities historical document image classification Vision Transformer ab: We present a novel approach for automatically classifying illustrations from historical Chinese local gazetteers using modern deep learning techniques. Our goal is to facilitate the digital organization and study of a large quantity of digitized local gazetteers. We evaluate the performance of eight state-of-the-art deep neural networks on a dataset of 4,309 manually labeled and organized images of Chinese local gazetteer illustrations, grouped into three coarse categories and nine fine classes according to their contents. Our experiments show that DaViT achieved the highest classification accuracy of 93.9 per cent and F1-score of 90.6 per cent. Our results demonstrate the effectiveness of deep learning models in accurately recognizing and categorizing historical local gazetteer illustrations. We also developed a user-friendly web service to enable researchers easy access to the developed models. The potential for extending this method to other collections of scanned documents beyond Chinese local gazetteers makes a significant contribution to the study of visual materials in the arts and history in the digital humanities field. The dataset used in this study is publicly available and can be used for further research in the field. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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