TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM

Abstract

This study presents Transfer Learning-based framework through the use of AlexNet for the development of an offline Yorùbá Handwritten Character Recognition System. The system encompasses the upper and case characters of the Yorùbá language, and tonal letters that have a significant impact on the Yorùbá language. The model reported network accuracy of 82.8%, validation accuracy of 77.7%, with F1 score of 0.7795, precision of 0.7819 and Recall of 0.7771. While the average recognition time is estimated to 0.371372 seconds. Thus, the technique of deep learning has shown significant improvement when compared to other existing approaches in recognizing standard Yorùbá characters.