English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Image Classification for Historical Documents: A Study on Chinese Local Gazetteers

Chen, J.-A., Hou, J.-C., Tsai, R.-T.-H., Liao, H.-M., Chen, S.-P., & Chang, M.-C. (2024). Image Classification for Historical Documents: A Study on Chinese Local Gazetteers. Digital Scholarship in the Humanities, 39(1), 61-73. doi:10.1093/llc/fqad065.

Item is

Files

show Files
hide Files
:
fqad065.pdf (Any fulltext), 2MB
Name:
fqad065.pdf
Description:
-
OA-Status:
Gold
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show
hide
Description:
-
OA-Status:
Not specified

Creators

show
hide
 Creators:
Chen, Jhe-An, Author
Hou, Jen-Chien, Author
Tsai, Richard Tzong-Han, Author
Liao, Hsiung-Ming, Author
Chen, Shih-Pei1, Author           
Chang, Ming-Ching, Author
Affiliations:
1Department Artifacts, Action, Knowledge, Max Planck Institute for the History of Science, Max Planck Society, ou_2266697              

Content

show
hide
Free keywords: -
 MPIWG_PROJECTS: Local Gazetteers
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2024-11-242024
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1093/llc/fqad065
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Digital Scholarship in the Humanities
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 39 (1) Sequence Number: - Start / End Page: 61 - 73 Identifier: ISBN: 2055-7671