Deep Learning Model Achieves 93.52% Accuracy in Ancient Ge’ez Manuscript Recognition

Researchers from Ethiopia have developed a deep convolutional neural network capable of recognizing characters in ancient Ethiopic Ge’ez manuscripts with an accuracy of 93.52%.

The experimental results demonstrated that the sequentially developed deep convolutional neural network achieved the highest performance, with a validation accuracy of 93.52%.

The successful application of deep convolutional neural networks to Ge’ez character recognition provides a foundation for future research in this area.

Subject of Research: Character recognition of ancient ethiopic Ge'ez manuscripts using deep convolutional neural networks Article Title: Character recognition of ancient ethiopic Ge'ez manuscripts using deep convolutional neural networks Article References: Guangul, K. A., Girmaw, D. W., & Meshesha, M. (2025).

Character recognition of ancient ethiopic Ge'ez manuscripts using deep convolutional neural networks.