Improvement accuracy of recognition isolated Balinese characters with Deep Convolution Neural Network

Authors

  • Ida Bagus Teguh Teja Murti Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.33633/jais.v4i1.2289

Abstract

The numbers of Balinese script and the low quality of palm leaf manuscripts provide a challenge for testing and evaluation for character recognition. The aim of high accuracy for character recognition of Balinese script,we implementation Deep Convolution Neural Network using SmallerVGG (Visual Geometry Group) Architectur for character recognition on palm leaf manuscripts. We evaluated the performance that methods and we get accuracy 87,23% .

References

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M. W. A. Kesiman, S. Prum, J. C. Burie, dan J. M. Ogier, “Study on feature extraction methods for character recognition of Balinese script on palm leaf manuscript images,†Proc. - Int. Conf. Pattern Recognit., hal. 4017–4022, 2017.

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Published

2019-07-16

Issue

Vol. 4 No. 1 (2019): Journal of Applied Intelligent System

Section

Articles

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