Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition
Many inscriptions in Bali are damaged. Damage to these inscriptions can be caused by natural disasters, overgrown with moss, algae and bacteria. Damage can also be caused by warfare, or deliberately erased. This inscription contains the knowledge and civilization of the ancestors so it is very impor...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2025-01-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/116841 |
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| _version_ | 1832575526309462016 |
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| author | Ida Ayu Putu Febri Imawati Made Sudarma I Ketut Gede Darma Putra I Putu Agung Bayupati Minho Jo |
| author_facet | Ida Ayu Putu Febri Imawati Made Sudarma I Ketut Gede Darma Putra I Putu Agung Bayupati Minho Jo |
| author_sort | Ida Ayu Putu Febri Imawati |
| collection | DOAJ |
| description | Many inscriptions in Bali are damaged. Damage to these inscriptions can be caused by natural disasters, overgrown with moss, algae and bacteria. Damage can also be caused by warfare, or deliberately erased. This inscription contains the knowledge and civilization of the ancestors so it is very important to be able to read its contents. Based on these problems, this research conducted training from scratch on 3 CNN models namely VGG16, MobileNetV1 and Simple CNN. The purpose of this research is to choose one recognition model that has the best performance and produces the highest recognition rate to proceed to the inscription restoration stage. The dataset used is Balinese inscription: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images in Challenge-3-ForTrain.zip. The training process of three models with five different training files resulted in the finding that VGG16 has the highest accuracy in the training, testing, and validation process with the least number of epochs. This research contributes to specific datasets, such as the Isolated Character Recognition of Balinese Script using the training process from the beginning of VGG16, involving all stages of the process. It will produce the best model performance compared to the other four training models. |
| format | Article |
| id | doaj-art-79aed4c23b284d8dbd66a0ef3f00113c |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-79aed4c23b284d8dbd66a0ef3f00113c2025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322025-01-01150314916010.24843/LKJITI.2024.v15.i03.p01116841Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription RecognitionIda Ayu Putu Febri Imawati0Made Sudarma1I Ketut Gede Darma Putra2I Putu Agung Bayupati3Minho Jo4Universitas Udayana, Universitas PGRI Mahadewa IndonesiaUdayana UniversityUdayana UniversityUdayana UniversityKorea UniversityMany inscriptions in Bali are damaged. Damage to these inscriptions can be caused by natural disasters, overgrown with moss, algae and bacteria. Damage can also be caused by warfare, or deliberately erased. This inscription contains the knowledge and civilization of the ancestors so it is very important to be able to read its contents. Based on these problems, this research conducted training from scratch on 3 CNN models namely VGG16, MobileNetV1 and Simple CNN. The purpose of this research is to choose one recognition model that has the best performance and produces the highest recognition rate to proceed to the inscription restoration stage. The dataset used is Balinese inscription: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images in Challenge-3-ForTrain.zip. The training process of three models with five different training files resulted in the finding that VGG16 has the highest accuracy in the training, testing, and validation process with the least number of epochs. This research contributes to specific datasets, such as the Isolated Character Recognition of Balinese Script using the training process from the beginning of VGG16, involving all stages of the process. It will produce the best model performance compared to the other four training models.https://ojs.unud.ac.id/index.php/lontar/article/view/116841 |
| spellingShingle | Ida Ayu Putu Febri Imawati Made Sudarma I Ketut Gede Darma Putra I Putu Agung Bayupati Minho Jo Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition Lontar Komputer |
| title | Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition |
| title_full | Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition |
| title_fullStr | Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition |
| title_full_unstemmed | Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition |
| title_short | Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition |
| title_sort | training vgg16 mobilenetv1 and simple cnn models from scratch for balinese inscription recognition |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/116841 |
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