  Few-shot learning is an important, but challenging problem of machine
learning aimed at learning from only fewer labeled training examples. It has
become an active area of research due to deep learning requiring huge amounts
of labeled dataset, which is not feasible in the real world. Learning from a
few examples is also an important attempt towards learning like humans.
Few-shot learning has proven a very good promise in different areas of machine
learning applications, particularly in image classification. As it is a recent
technique, most researchers focus on understanding and solving the issues
related to its concept by focusing only on common image datasets like
Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to
address low resource languages like Amharic. In this study, offline handwritten
Amharic character recognition using few-shot learning is addressed.
Particularly, prototypical networks, the popular and simpler type of few-shot
learning, is implemented as a baseline. Using the opportunities explored in the
nature of Amharic alphabet having row-wise and column-wise similarities, a
novel way of augmenting the training episodes is proposed. The experimental
results show that the proposed method outperformed the baseline method. This
study has implemented few-shot learning for Amharic characters for the first
time. More importantly, the findings of the study open new ways of examining
the influence of training episodes in few-shot learning, which is one of the
important issues that needs exploration. The datasets used for this study are
collected from native Amharic language writers using an Android App developed
as a part of this study.
