End-to-end handwritten Ge’ez multiple numerals recognition using deep learning

Abstract

Ge'ez has been used in Ethiopian churches for centuries to read and interpret the Bible. As a part of the liturgy and religious ceremonies, it is also utilized in prayer and chanting. Since the language is ancient, plenty of handwritten physical documents are generated. Recognizing handwritten Ge'ez numerals poses significant challenges due to variations in handwriting styles, incomplete or overlapping strokes, noise, and distortion. This study introduces an end-to-end approach for handwritten Ge'ez multiple numeral recognition employing deep neural networks. The proposed method streamlines recognition by eliminating manual feature extraction stages. To enable end-to-end training without explicit alignment, the model uses attention mechanisms and a connectionist temporal classification-based loss function. The proposed model is evaluated on 120,000 handwritten Ge'ez multiple numeral images, which is a syntactically generated dataset. The developed recognition model achieved a character error rate (CER) of 2.81% and a word error rate (WER) of 18.13%. These results highlight the effectiveness of the approach in accurately and reliably recognizing handwritten Ge'ez multiple numerals.