# Offline Handwritten Amharic Character Recognition Using Few-shot Learning

arXiv: https://arxiv.org/abs/2210.00275 · HTML: https://mlsanity.com/paper/2210.00275/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples](https://mlsanity.com/paper/1903.03096/) (2019)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Leveraging the Feature Distribution in Transfer-based Few-Shot Learning](https://mlsanity.com/paper/2006.03806/) (2020)
- [The Omniglot challenge: a 3-year progress report](https://mlsanity.com/paper/1902.03477/) (2019)
- [Gaussian Prototypical Networks for Few-Shot Learning on Omniglot](https://mlsanity.com/paper/1708.02735/) (2017)
- [Improving Few-Shot Learning using Composite Rotation based Auxiliary Task](https://mlsanity.com/paper/2006.15919/) (2020)
- [Multi-script Handwritten Digit Recognition Using Multi-task Learning](https://mlsanity.com/paper/2106.08267/) (2021)
- [Improving Amharic Handwritten Word Recognition Using Auxiliary Task](https://mlsanity.com/paper/2202.12687/) (2022)

## Built on by (most cited first, 2 of 2)

- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)
- [DNA: Dual-branch Network with Adaptation for Open-Set Online Handwriting Generation](https://mlsanity.com/paper/2511.22064/) (2025)

## Abstract (arXiv)

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.