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Handwritten Changma Numerals Recognition Using Capsule Networks

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dc.contributor.author Hasan, Md Zahid
dc.contributor.author Hasan, K. M. Zubair
dc.contributor.author Hossain, Shakhawat
dc.contributor.author Al Mamun, Abdullah
dc.contributor.author Assaduzzaman, Md
dc.date.accessioned 2022-03-01T06:33:30Z
dc.date.available 2022-03-01T06:33:30Z
dc.date.issued 2019-09-28
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7334
dc.description.abstract Handwritten digit recognition is assumed to be a huge part in numerous authentication applications in the state-of-art technologies. As the manually written digits are not always found in similar size, thickness, style and orientation, recognition of handwritten digits is hardly possible in many cases. To address this recognition tasks using handwritten digits, a great deal of work has been conducted with different image processing technologies on a variety of non-Indic's languages. However, this paper represents a handwritten digit recognition system of recognizing Changma Vaj Digit Recognition (CVDR). The digit recognition approach used in this paper is capsule network, an improved version of Convolutional Neural Network (CNN). The proposed system has been trained with more than 3440 sample image and been tested with more than 860 images. The proposed paper also presents a graphical comparison between the recognition accuracy performed by CNN and CapsNet, which in other terms demonstrates the supremacy of CapsNet's accuracy by 5.7%. en_US
dc.language.iso en_US en_US
dc.publisher 2019 5th International Conference on Advances in Electrical Engineering, IEEE en_US
dc.subject Changma handwritten numerals recognition en_US
dc.subject Multi-digits recognition en_US
dc.subject Capsul net en_US
dc.subject Neural network en_US
dc.subject CNN en_US
dc.subject CVDR en_US
dc.subject Dataset en_US
dc.title Handwritten Changma Numerals Recognition Using Capsule Networks en_US
dc.type Article en_US

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