Ethiopic Characters Recognition Model using Deep Learning

Abstract

Abstract Purpose: -Many approaches have been developed to recognize different writing scripts, most of which focus on Latin scripts. However, in the Ethiopic script unavailability of readymade datasets and the attempts of a few studies show that the script requires further investigation by applying different recognition techniques. In addition, image quality degradation and numerous characters in the alphabet are other major problems with Ethiopic-script documents. In this study, a convolutional neural network-based character recognition model for Ethiopic character recognition is proposed. The contribution of this work is the development of a character recognition model using a convolutional neural network. Method: -The model is designed using seven convolutional layers, four max-pooling layers, and two fully connected layers. Automatic features are extracted from each cropped 28 by 28 images using convolutional and max-pooling layers to prepare for recognition. Results: -The proposed model is trained and tested with customized datasets and achievement of accuracy 97%, which is a promising recognition result of the script. Conclusion: -In future works, the dataset should include all the letters, punctuation marks, and symbols found in Ethiopic scripts for both printed and handwritten text and increase the dataset size for different deep learning algorithms to get better results.