Model Sequential Resnet50 Untuk Pengenalan Tulisan Tangan Aksara Arab

Sarwati Rahayu (Universitas Mercu Buana)
Sulis Sandiwarno (Universitas Mercu Buana)
Erwin Dwika Putra (Universitas Muhammadiyah Bengkulu)
Marissa Utami (Universitas Muhammadiyah Bengkulu)
Hadiguna Setiawan (ESC Technology)

JSAI (Journal Scientific and Applied Informatics)
Vol 6 No 2 (2023): Juni

Research for Arabic handwriting recognition is still limited. The number of public datasets regarding Arabic script is still limited for this type of public dataset. Therefore, each study usually uses its dataset to conduct research. However, recently public datasets have become available and become research opportunities to compare methods with the same dataset. This study aimed to determine the implementation of the transfer learning model with the best accuracy for handwriting recognition in Arabic script. The results of the experiment using ResNet50 are as follows: training accuracy is 91.63%, validation accuracy is 91.82%, and the testing accuracy is 95.03%.