HANDWRITTEN CHARACTER RECOGNITION IN ASSYRIAN LANGUAGE USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Academics and researchers worldwide have paid close attention to biometric handwriting recognition using deep learning as much research has been proposed to enhance biometric recognition in the past and in recent years. Several solutions for character recognition systems in various languages, including Chinese, English, Japanese, Arabic, and Kurdish have been developed. Unfortunately, there has been minimal growth in the Assyrian language. There is still little research on Assyrian handwriting. In this paper, a new Assyrian language dataset was created as part of the procedure by distributing 500 forms consisting of 36 Assyrian characters to people between the ages of 13 and 60 of both genders. The preprocessing operation includes cleaning the noisy data and segmenting each image to 224x224 pixels. This effort resulted in the collection of 18,000 images of these characters to be trained 70% and tested 30% in four CNN models, VGG16, VGG19, MobileNet-V2, and ResNet-50, over 30 epochs to give an accuracy rate of 90.97%, 92.06%, 95.70%, and 94.97%., respectively.