Japanese Historical Character Recognition using Deep Convolutional Neural Network (DCNN) with DropBlock Regularization
Sujata Saini1, Vishal Verma2 

1Sujata Saini, Department of Computer Science and Applications, Chaudhary Ranbir Singh University, Rohtak, India.
2Vishal Verma, Department of Computer Science and Applications, Chaudhary Ranbir Singh University, Jind, India.

Manuscript received on 11 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 3510-3515 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2923078219/19©BEIESP | DOI: 10.35940/ijrte.B2923.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The history of a country always helps in the development of making modern society. Japanese classical handwritten/literature dataset Kuzushiji-MNIST and Kuzushiji-49 recently introduced in public domain by Center of Open Data in the Humanities (CODH). The availability of the datasets invite the researchers in different domains. The hidden treasure of information in these datasets resolved and worked by many researchers. The research on these datasets can be helpful to explore and analyze the detail of Japanese era/literature, which ultimately helps in the development of modern society. In this study, Deep Convolutional Neural Network (DCNN) with Drop Block regularization is used to recognize the hiragana characters in Japanese historical script.
Index Terms: Japanese Handwritten Character Recognition, Historical Document’s Classification, Deep Convolutional Neural Network (DCNN), Deep Learning, Drop Block Regularization.

Scope of the Article: Classification