  Amharic is the official language of the Federal Democratic Republic of
Ethiopia. There are lots of historic Amharic and Ethiopic handwritten documents
addressing various relevant issues including governance, science, religious,
social rules, cultures and art works which are very reach indigenous knowledge.
The Amharic language has its own alphabet derived from Ge'ez which is currently
the liturgical language in Ethiopia. Handwritten character recognition for non
Latin scripts like Amharic is not addressed especially using the advantages of
the state of the art techniques. This research work designs for the first time
a model for Amharic handwritten character recognition using a convolutional
neural network. The dataset was organized from collected sample handwritten
documents and data augmentation was applied for machine learning. The model was
further enhanced using multi-task learning from the relationships of the
characters. Promising results are observed from the later model which can
further be applied to word prediction.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning