Handwritten Ge?��?ez Digit Recognition Using Deep Learning

Abstract

Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. Many historical documents are written using the Ge?��?ez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Ge?��?ez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional Neural Network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this thesis, the pro