skip to main content
10.1145/3209280.3229117acmconferencesArticle/Chapter ViewAbstractPublication PagesdocengConference Proceedingsconference-collections
short-paper

A Handwritten Japanese Historical Kana Reprint Support System: Development of a Graphical User Interface

Published:28 August 2018Publication History

ABSTRACT

Reprint of Japanese historical manuscripts is time-consuming and requires training because they are hand-written, and may contain characters different from those currently used. We proposed a framework for assisting the human process for reading Japanese historical manuscripts and implemented a part of a system based on the framework as a Web service. In this paper, we present a graphical user interface (GUI) for the system and reprint process through the GUI. We conducted a user test to evaluate the system with the GUI by a questionnaire. From the results of the experiment, we confirmed that the GUI can be used intuitively but we also found points to be improved in the GUI.

References

  1. Yuta Arai, Tetsuya Suzuki, and Akira Aiba. 2013. Recognizing Historical KANA Texts Using Constraints. Springer Japan, Tokyo, 151--164.Google ScholarGoogle Scholar
  2. Richard G. Casey and Eric Lecolinet. 1996. A Survey of Methods and Strategies in Character Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 7 (July 1996), 690--706. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bjarke Frellesvig. 2010. A History of the Japanese Language. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Taichi Hayasaka, Wataru Ohno, Yumie Kato, and Kazuaki Yamamoto. 2017. Recognition of Kuzushiji (Hentaigana and cursive script) by Deep Learning (ver.0.4.1). (2017). http://vpac.toyota-ct.ac.jp/kuzushiji/Google ScholarGoogle Scholar
  5. Taichi Hayasaka, Wataru Ohno, Yumie Kato, and Kazuaki Yamamoto. 2017. Trial Production of Application Software for Machine Transcription of Hentaigana by Deep Learning. In Proceedings of the 31st Annual Conference of the Japanese Society for Artificial Intelligence.Google ScholarGoogle Scholar
  6. Laurence Likforman-Sulem, Abderrazak Zahour, and Bruno Taconet. 2007. Text line segmentation of historical documents: a survey. International Journal of Document Analysis and Recognition (IJDAR) 9, 2 (01 Apr 2007), 123--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ryuichi Oka. 1998. Spotting Method for Classification of Real World Data. Comput. J. 41, 8 (1998), 559--565.Google ScholarGoogle ScholarCross RefCross Ref
  8. Tamekazu Reizei. 1994. Tales of Ise (photocopy). Kasama Shoin.Google ScholarGoogle Scholar
  9. Kazuki Sando, Tetsuya Suzuki, and Akira Aiba. 2018. A Constraint Solving Web Service for Recognizing Historical Japanese KANA Texts. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,. INSTICC, SciTePress, 257--265.Google ScholarGoogle ScholarCross RefCross Ref
  10. Kengo Terasawa and Toshio Kawashima. 2011. Word Spotting Online. In Proceedings of the Computers and the Humanities Symposium, Vol. 2011. 329--334.Google ScholarGoogle Scholar
  11. Satoru Watanabe, Tetsuya Suzuki, and Akira Aiba. 2015. Reducing of the Number of Solutions Using Adjacency Relation of Words in Recognizing Historical KANA Texts. IPSJ Journal 56, 3 (mar 2015), 951--959. http://ci.nii.ac.jp/naid/110009884088/en/Google ScholarGoogle Scholar
  12. Shoji Yamada and Mamoru Shibayama. 2003. An Estimation Method of Unreadable Historical Character for Manuscripts in Fixed Forms using n - gram and OCR. IPSJ SIG Notes 2003, 59 (may 2003), 17--24. http://ci.nii.ac.jp/naid/110002911078/en/Google ScholarGoogle Scholar
  13. Sumiko Yamamoto and Tomejiro Osawa. 2016. Labor saving for reprinting Japanese rare classical books. Journal of Information Processing and Management 58, 11 (2016), 819--827.Google ScholarGoogle Scholar

Index Terms

  1. A Handwritten Japanese Historical Kana Reprint Support System: Development of a Graphical User Interface

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      DocEng '18: Proceedings of the ACM Symposium on Document Engineering 2018
      August 2018
      311 pages
      ISBN:9781450357692
      DOI:10.1145/3209280

      Copyright © 2018 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 August 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate178of537submissions,33%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader