Classification of Handwritten Sundanese Script via Transfer Learning on CNN-Based Architectures
Masayu Anandita Prameswari, Mahmud Dwi Sulistiyo, Aditya Firman Ihsan · 2023
The Sundanese script is a cultural heritage of the Sundanese people that deserves to be preserved. With the advances in computer vision and deep learning, the study of Indonesian local character recognition still has room for improvement and therefore needs to be encouraged. In this study, the Sundanese script used was Swara and Ngalagena script. The classification of the Sundanese script poses a significant challenge due to the complexity and variability of the script's visual patterns. Therefore, this study classifies Sundanese script using the deep learning method, namely the Transfer Learning method based on the Convolutional Neural Network. The architectures used in this research are ResNet-50, VGG-19, and MobileNet. Then to evaluate the performance of the three architectures, the evaluation metrics used in this study are Accuracy, Precision, Recall, and F1-scores will be used. The three CNN architectures show very satisfactory results in classifying Sundanese scripts. VGG-19 architecture Accuracy is 99% for training data and 95% for validation data, the ResNet-50 architecture is 97%, and for validation data is 91%. Meanwhile, the MobileNet architecture data train produces an Accuracy of 97% and data validation of 92%. Overall. The VGG-19 architecture is the best in this Sundanese script classification study.