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Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation
Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. (Mobile and Pervasive Computing)ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
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2019 (English)In: Proceedings of the Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Convolutional Neural Network, handwritten character recognition, Bangla handwritten characters, Data augmentation
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-73307OAI: oai:DiVA.org:ltu-73307DiVA, id: diva2:1298999
Conference
Joint 2019 8th International Conference on Informatics, Electronics & Vision (ICIEV), 26 - 29 April 2019, Spokane, United States
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251Available from: 2019-03-25 Created: 2019-03-25 Last updated: 2019-04-02Bibliographically approved

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Type fulltextMimetype application/pdf

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Islam, Raihan UlAndersson, Karl

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CiteExportLink to record
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