MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network

The lontar manuscript is an ancient Balinese cultural heritage written using Balinese characters on palm leaves. The recognition of Balinese characters in lontar is challenging because it has noise and limited data availability. To solve these problems, data augmentation is needed to increase the va...

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Autores principales:	Ni Putu Sutramiani, Nanik Suciati, Daniel Siahaan
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Balinese character Lontar manuscript Data augmentation Adaptive Gaussian Thresholding Convolutional Autoencoder Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/1eb1dcb8d5cc44b2b49090584e2410d1
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id	oai:doaj.org-article:1eb1dcb8d5cc44b2b49090584e2410d1
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spelling	oai:doaj.org-article:1eb1dcb8d5cc44b2b49090584e2410d12021-11-30T04:16:42ZMAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network2405-959510.1016/j.icte.2021.04.005https://doaj.org/article/1eb1dcb8d5cc44b2b49090584e2410d12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000497https://doaj.org/toc/2405-9595The lontar manuscript is an ancient Balinese cultural heritage written using Balinese characters on palm leaves. The recognition of Balinese characters in lontar is challenging because it has noise and limited data availability. To solve these problems, data augmentation is needed to increase the variety and amount of data to improve recognition performance. In this study, we collected Balinese character images from 50 lontar manuscript writers. We proposed MAT-AGCA that combines Adaptive Gaussian Thresholding and Convolutional Autoencoder for data augmentation. Based on experiments using InceptionResnetV2, DenseNet169, ResNet152V2, VGG19, and MobileNetV2, our proposed method achieved the best performance with 96.29% accuracy.Ni Putu SutramianiNanik SuciatiDaniel SiahaanElsevierarticleBalinese characterLontar manuscriptData augmentationAdaptive Gaussian ThresholdingConvolutional AutoencoderInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 521-529 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Balinese character Lontar manuscript Data augmentation Adaptive Gaussian Thresholding Convolutional Autoencoder Information technology T58.5-58.64
spellingShingle	Balinese character Lontar manuscript Data augmentation Adaptive Gaussian Thresholding Convolutional Autoencoder Information technology T58.5-58.64 Ni Putu Sutramiani Nanik Suciati Daniel Siahaan MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
description	The lontar manuscript is an ancient Balinese cultural heritage written using Balinese characters on palm leaves. The recognition of Balinese characters in lontar is challenging because it has noise and limited data availability. To solve these problems, data augmentation is needed to increase the variety and amount of data to improve recognition performance. In this study, we collected Balinese character images from 50 lontar manuscript writers. We proposed MAT-AGCA that combines Adaptive Gaussian Thresholding and Convolutional Autoencoder for data augmentation. Based on experiments using InceptionResnetV2, DenseNet169, ResNet152V2, VGG19, and MobileNetV2, our proposed method achieved the best performance with 96.29% accuracy.
format	article
author	Ni Putu Sutramiani Nanik Suciati Daniel Siahaan
author_facet	Ni Putu Sutramiani Nanik Suciati Daniel Siahaan
author_sort	Ni Putu Sutramiani
title	MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
title_short	MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
title_full	MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
title_fullStr	MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
title_full_unstemmed	MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network
title_sort	mat-agca: multi augmentation technique on small dataset for balinese character recognition using convolutional neural network
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/1eb1dcb8d5cc44b2b49090584e2410d1
work_keys_str_mv	AT niputusutramiani matagcamultiaugmentationtechniqueonsmalldatasetforbalinesecharacterrecognitionusingconvolutionalneuralnetwork AT naniksuciati matagcamultiaugmentationtechniqueonsmalldatasetforbalinesecharacterrecognitionusingconvolutionalneuralnetwork AT danielsiahaan matagcamultiaugmentationtechniqueonsmalldatasetforbalinesecharacterrecognitionusingconvolutionalneuralnetwork
_version_	1718406790151929856

MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network

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