# Offline Handwritten Amharic Character Recognition Using Few-shot Learning

Mesay Samuel1, Lars Schmidt-Thieme2, DP Sharma3, Abiot Sinamo4, Abey
Bruck11Arba Minch University, Faculty of Computing and Software Engineering,
Ethiopia<EMAIL_ADDRESS>
2Information Systems and Machine Learning Lab, 31141 Hildesheim, Germany,
<EMAIL_ADDRESS>
3AMUIT MOEFDRE under UNDP<EMAIL_ADDRESS>
4Ministry of Innovation and Technology, Federal Democratic Republic of
Ethiopia<EMAIL_ADDRESS>
Abstract—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.

Keywords: Few-shot Learning, Amharic Handwritten Recognition, Prototypical
Networks, Training Episode

## 1 Introduction

Few-shot learning can be defined as a type of machine learning which aims at
gaining good learning performance with few (usually 20 or less) supervised
training examples [1]. It is an important area of machine learning which
contributes to the advancement in AI in the aspect of learning humanly as
humans easily learn from fewer examples. It also helps in learning rare cases
which can be applied to fraud detection in electronic transactions. Few-shot
learning can be applied to different application domains including computer
vision, robotics, natural language processing, acoustic signal processing,
drug discovery, and the like. Few-shot learning also reduces the data
gathering effort and computational cost associated with big datasets which are
very common issues in deep learning.

Various techniques are applied to address the problem of few-shot learning. In
all the cases however, there is a way to exploit prior knowledge accumulated
in the data, model or algorithm of any related machine learning task. The most
common and effective way is through algorithm approach particularly meta-
learning. In meta-learning an attempt is to improve performance of a new task
by the meta-knowledge extracted across related tasks through a meta-learner
[1]. Hence, the formulation of tasks plays an important role in such problems.
These tasks are also known as episodes having their own training and test
sets. These training and test sets are also called support and query sets
respectively in few-shot learning terminologies. Each task has the same number
of classes (referred as ways) for the support and query sets. However, the
number of examples per class in the support set only defines the shot. Hence,
a 3 way 5 shot few-shot learning problem describes a task formulation with 3
classes and 5 examples per class in the support set.

Meta-learning also known as learning to learn is any type of learning based on
prior learning experience with other tasks. The similarity of the tasks also
plays an important role in a way that the more similar those tasks are, the
more types of meta-data one can leverage, and defining task similarity remains
the key challenge. Other types of learning including multi-task learning,
transfer learning and ensemble learning can also be meaningfully combined with
meta-learning systems. Hence, the scientific contributions in meta-learning
speed up and improve the design of machine learning pipelines and also allow
us to replace hand-engineered algorithms with novel approaches learned in a
data-driven way [2]. Likewise this study explores and opens a wide range of
perspectives into examining task similarities and their related effects on
specific applications of deep learning and few-shot learning.

Amharic optical character recognition in general and the handwritten character
recognition in particular is not a well studied area of research. The
unavailability of standard public datasets make Amharic as one of the low
resource languages. Even though there are limited attempts, most of these
works focus on implementation of off-the-shelf inventions which are
particularly designed for Latin scripts. This trend has created two
interconnected problems. The first one is associated with the limitations to
fit the real problem and proposed solution. Another problem arises from
overlooking the opportunities that might have emerged with any possible
innovations from the exploration of specific contexts which can then be scaled
up to generalized solutions [3, 4, 5, 6].

In this study, offline handwritten Amharic character recognition is addressed
using few-shot learning for the first time. Few-shot learning is a recent and
promising area of research resolving the limitations of deep learning which
requires huge amounts of labeled data. Accordingly, such techniques open a way
to address low resource languages like Amharic. It is also suitable to address
the issue of rarely occurring characters in real life documents. More
importantly in this study, training episodes are examined from the context and
nature of Amharic characters, which are the core issues in few-shot learning
problems. Most of the studies in few-shot learning focus on understanding the
problem itself and hence are based on common image datasets like Mini-ImageNet
and Omniglot. However, in this study a more realistic application of few-shot
learning is presented using prototypical networks which is a popular and
simpler type of few-shot learning.

The challenges facing deep learning studies when it comes to low resource
languages is not only unavailability of huge standard datasets but also the
difficulty of training deep learning architectures considering the fact that
they typically are made of way more parameters than the dataset contains [7].
In regard to few-shot learning also, the datasets used to assess are not
challenging and realistic as compared to the progress made in the techniques
and models [8]. Hence in this study, a suitable few-shot learning dataset is
organized for Amharic characters using an Android App developed as a part of
this study. Generally, the contributions of this paper can be summarized as
follows:

  1. i.

Organized a new few-shot learning dataset for Amharic handwritten characters
with the appropriate split of train, validation, and test sets.

  2. ii.

Implemented few-shot learning for Amharic handwritten characters recognition
for the first time as a benchmark.

  3. iii.

Empirically explored how training episodes affect the performance in few-shot
learning with a novel contribution in the context of Amharic handwritten
characters recognition.

## 2 Related Work

Different recent papers have emerged to clarify the progress in few-shot
learning [8, 9, 10, 11]. These studies mainly address the problems associated
with few-shot learning datasets and performance measures. Triantafillou et al.
[8] proposed META-DATASET: a new benchmark for training and evaluating few-
shot learning models that is large-scale, consists of diverse datasets, and
presents more realistic tasks. Dhillon et al. [9] performed extensive studies
on benchmark datasets to propose a metric that quantifies the hardness of a
few-shot episode which can be used to report the performance of few-shot
algorithms in a more systematic way.

Wang et al. [1] have made a rigorous review on few-shot learning problems to
formally define and construct a good taxonomy of few-shot learning problems.
Authors gave a formal definition of few-shot learning which connects to the
general problem of machine learning by illustrating the unreliable empirical
risk minimizer. That is the core issue in few-shot learning which arises due
to fewer examples identified based on error decomposition in supervised
machine learning. Therefore, few-shot learning techniques should find a way to
use prior knowledge accumulated in data, model, and algorithm of other related
tasks. Accordingly, Wang et al. [1] classified few-shot learning methods by
their focus on these three constituents into data (augment training dataset
using prior knowledge), model (constrain hypothesis space by prior knowledge),
and algorithm (alter search strategy in hypothesis space by prior knowledge).

Typical examples of algorithm few-shot learning are Model-Agnostic Meta-
Learning (MAML) [12] and its variants like Reptile [13]. These methods learn
parameter initialization that can be fine-tuned quickly for a new task. MAML
learns initialization through effective gradient steps for a new task with a
small amount of training data to produce good generalization. Reptile works
this by repeatedly sampling a task, training on it, and moving the
initialization towards the trained weights on that task [1, 12, 13]. Another
set of Model few-shot learning methods include siamese neural networks,
matching networks, and prototypical networks which are task-invariant
embedding learning models [1]. These methods are also known as metric learning
as they learn to classify new images based on their similarity to support
images unlike gradient-based meta-learning which leverages gradient descent to
learn commonalities among various tasks [14, 15, 16]. Simple Neural Attentive
Learner (SNAIL) is another embedding network with interleaved temporal
convolution layers and attention layers which presents an alternative paradigm
where a generic architecture has the capacity to learn an algorithm that
exploits domain-specific task structure [1, 17]. In this paper, both model and
algorithm few-shot methods are exhibited due to the implementation of
prototypical networks and incorporation of auxiliary task episodic training.

More related papers address few-shot learning from two main perspectives which
are interrelated by nature. The first one is focusing on extraction of highly
discriminative features which can easily generalize classes so that very few
samples would be sufficient. Another direction is looking for different
possible auxiliary or related tasks that can be used to complement the main
few-shot classification task. This can be done by proposing creative
classification tasks that can be trained in a multi-task learning fashion
[18]. Mazumder et al. [18] proposed an approach which uses self-supervised
auxiliary tasks to produce highly discriminative generic features from image
datasets. The auxiliary task is a two level rotation of patches including
inside the image and rotation of the whole image and assigning one out of 16
rotation classes to the modified image. When these tasks are trained
simultaneously with the main classification task, the network learns high-
quality generic features that help improve the few-shot classification
performance. Such methods actually utilize the concept of gradient/
optimization-based meta-learning approach of few-shot learning. Accordingly, a
related work by Tripathi et al. [19] further integrated both induction and
transduction into the base learner in an optimization-based meta-learning
framework. On the other hand Ravi and Larochelle [20], rather than training a
single model over multiple episodes, introduced an LSTM meta-learner which
learns to train a custom model for each few-shot episode.

A simpler and more efficient approach to few-shot learning is prototypical
networks which is a metric learning. The main idea is that there exists an
embedding in which points cluster around a single prototype representation for
each class. Hence, the networks learn a metric space in which classification
can be performed by computing distances to prototype representations of each
class. Prototypical networks learn a non-linear mapping of the input into an
embedding space using neural networks and take a class’s prototype to be the
mean of its support set in the embedding space. Classification is then
performed for an embedded query point by simply finding the nearest class
prototype [16]. Building on prototypical networks, Fort [21] extended to
Gaussian prototypical networks incorporating a Gaussian covariance matrix,
where the network constructs a direction and class dependent distance metric
on the embedding space, using uncertainties of individual data points as
weights.

## 3 Methodology

### 3.1 Dataset Preparation

The dataset used for this study is collected using an Android app developed as
part of this study. Screenshots of the app are shown in Figure 1. The link to
the app with a brief notice was distributed using different platforms
including email, Facebook messenger, Telegram and WhatsApp to different groups
of individuals who can write Amharic. For this few-shot learning study, a
total of 1,325 handwritten character images were organized. That is, five
images per character for 265 Amharic characters in the Amharic alphabet. The
five character images are randomly selected from those written by more than 35
individuals. The images are resized to $32\times 32$ pixels and are grouped
into 120, 61, and 84 unique sets of characters for train, validation, and test
splits respectively. A table showing the specific characters used for these
splits is available in Appendix A. The dataset of this study is publicly
available111https://github.com/mesaysama/amharic-handwritten-character-dataset

Figure 1: Screenshots of the data collection app.

### 3.2 Prototypical Networks

This study implemented Prototypical Networks as a baseline. By exploiting the
opportunities from the Amharic alphabet having row-wise and column-wise
similarities, meaningful alterations are made in the training episodes for the
proposed methods. Prototypical Networks is one of the popular metric based
few-shot learning methods which classify new classes based on their similarity
to a small number of examples per class. This small number of classes is the
support set which is the only information used by a few-shot classification
model in order to classify query images. During training, Prototypical
Networks compute a prototype for each class, which is the mean of all
embeddings of support images from this class. Then, each query is simply
classified as the nearest prototype in the feature space, with respect to
euclidean distance. Whereas the support sets have labels both during training
and testing time, the query sets have labels only during training time. Hence,
for each image of the query set, the aim is to predict a label from the labels
present in the support set during testing time. This study used a pretrained
ResNet18 as a backbone to project both support and query images into a feature
space (embedding).

## 4 Results and Discussion

All the experiments in this study are implemented using Pytorch machine
learning library on the Google Colab and the computing cluster of Information
Systems and Machine Learning Lab (ISMLL) from the University of Hildesheim.
More importantly, the implementations are based on easy few-shot learning code
by Bennequin [22]. During this study, many few-shot learning experiments were
performed to explore the possible ways one can make episodic training for the
case of Amharic handwritten character recognition. For instance, one can train
a model with label A and test the model with label B which is not the case in
regular machine learning practice. Label A could be the Amharic character row
label and label B may refer to the specific character label in the Amharic
Alphabet. This is possible since a few-shot learning method allows testing a
new class with its own few supervised samples. Particularly, this is due to
the possibility of assigning an Amharic character to multiple labels from its
tabular alphabet as shown in Appendix A.

Even though there are many ways one can implement few-shot learning using
episodic training, this study is limited to meaningful insights and the most
common settings in the literature. Accordingly, this study implemented a few-
shot learning for 1,2 and 3 shots with 5 way settings only. In all the
settings, the query sets are kept to hold only two samples per class. A custom
data loader is used to organize and feed the model with the appropriate
combination of support and query set in each task. Training episodes are set
to 20,000 tasks which are randomly generated few-shot classification tasks.
The model iterates over these tasks to fit and update after each task which is
called episodic training. The model predicts the labels of the query set based
on the information from the support set; then it compares the predicted labels
to ground truth query labels, which gives us a loss value. In this study,
standard cross entropy loss and Adam optimizer are used.

For validation and testing experiments, a separate set of previously unseen
classes are used with 1000 tasks each. For the case of proposed methods, half
of the training episodes are replaced with row label and column label based
task formulations for the proposed method 1 and 2 respectively. Sample
datasets showing the task formulations for the baseline and proposed methods
are presented in Figure 2.

Figure 2: Sample dataset from episodes/ task formulation in the baseline and
proposed methods.

A total of nine models are experimented to compare the results between the
baseline and proposed methods for the three few-shot settings. Each experiment
is run three times and the average result of the accuracy scores on a test set
for 1000 character label classification task is presented in Table 4. As can
be seen from the results table, the baseline method scored 38.10%, 91.10%, and
93.70% accuracy for 1, 2, and 3 shot settings respectively. The proposed
method 1 scored 75.90%, 90.10%, and 88.40% for 1, 2, and 3 shot settings
respectively. Likewise, the proposed method 2 scored 38.30%, 87.00%, and
92.90% for 1, 2, and 3 shot settings respectively.

Table 1: Accuracy scores of the competing models on a test set under different
few-shot settings.

Methods |  1-shot |  2-shot |  3-shot
---|---|---|---
Baseline |  38.10% |  91.10% |  93.70%
Proposed Method 1 |  75.90% |  90.10% |  88.40%
Proposed Method 2 |  38.30% |  87.00% |  92.90%

An important finding of this study revealed the superior performance of the
proposed method 1 which uses row based episodic training. That is, 75.90%
accuracy which outperformed other methods by a significant margin in a 1-shot
setting. Even though the baseline method has a better performance for the 2
and 3-shot settings, still the proposed methods showed comparable results.
That is, the results of the baseline and proposed method 1 are relatively
closer to each other in a 2-shot setting. Similarly, the results of the
baseline and proposed method 2 are close to each other in the 3-shot setting.

Another important finding from this study is that both proposed methods have
their own relevance in the different few-shot settings. That is, the proposed
method 1 which uses row based episodic training helps when it is very few-
shot. In contrast, proposed method 2 which uses column based episodic training
appears to help when the shots are increasing. This is an interesting behavior
which needs further investigation to help Amharic handwritten character
recognition using few-shot learning. In line with this, both the baseline and
proposed method 2 have shown an increase in performance along with the
increase in shots. This is an expected empirical result in few-shot learning
as studied by Triantafillou et al. [8] on the effects varying the number of
shots and ways in different few-shot methods including prototypical networks.
Another study by Dhillon et al. [9] identified that using a large number of
meta-training classes results in high few-shot accuracies even for large
number of few-shot classes (ways). Hence, even though the effect of varying
ways is not investigated in this study, the large number of Amharic characters
in the alphabet and the experiments explored in this study open an interesting
area of further study.

The results of the proposed methods in this study have shown how episodic task
formulation affects the performance of few-shot learning. This supports the
emphasis given to meta-training distribution of episodes by different studies
[1, 18, 19, 20, 23]. However, as attempted to explore in this study, the
proportion and the pattern of formulating the tasks from character label, row
label, and col label will remain an open area of research. This is
particularly relevant during the training phase for few-shot learning since
the test sets contain previously unseen classes with their own labels in the
support set.

Finally, this study has benefited from both types of few-shot learning
approaches including the metric learning and optimization-based learning. The
use of prototypical networks helps to learn an embedding space which signifies
metric learning [19]. On the other hand, the study exploited the advantage of
transfer learning and multi-task learning through episode design in meta-
learning [23] which also improves generalization by enabling the extraction of
discriminative generic features [16, 18].

## 5 Conclusion

This study addressed offline handwritten Amharic character recognition using
few-shot learning for the first time. As a baseline method, this study
implemented prototypical networks which is an embedding and metric based few-
shot learning method. From the opportunities of Amharic alphabet having row-
wise and column-wise similarities, a novel way of augmenting the training
episodes is explored as a proposed method. The results of the study revealed
that the proposed method outperformed the baseline method by a significant
margin in a 5-way 1-shot few-shot learning setting. This study has also proven
how formulation of training episodes using related auxiliary tasks could
affect the performance of few-shot learning methods. The dataset prepared by
this study is another important contribution for Amharic few-shot learning
research by bringing a more suitable and realistic dataset which can be used
by other researchers.

Even though this study experimented with few-shot learning in different
settings by varying the shots, the effects of varying ways remain unexplored.
Hence, future studies can focus on extending the experiments to find the most
optimum few-shot learning setting. Studying the formulation of training
episodes from the combination of character, row, and column labels is also an
important area of research to progress in few-shot learning for Amharic
handwritten character recognition.

## References

  * [1] Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni,  “Generalizing from a few examples: A survey on few-shot learning”,  ACM computing surveys (csur), vol. 53, no. 3, pp. 1–34, 2020.
  * [2] Joaquin Vanschoren,  “Meta-learning: A survey”,  arXiv preprint arXiv:1810.03548, 2018.
  * [3] Efrem Yohannes Obsie, Hongchun Qu, and Qingqin Huang,  “Amharic Character Recognition Based on Features Extracted by CNN and Auto-Encoder Models”,  in 2021 The 13th International Conference on Computer Modeling and Simulation, 2021, pp. 58–66.
  * [4] Birhanu Hailu Belay, Tewodros Habtegebrial, Marcus Liwicki, Gebeyehu Belay, and Didier Stricker,  “A Blended Attention-CTC Network Architecture for Amharic Text-image Recognition.”,  in ICPRAM, 2021, pp. 435–441.
  * [5] Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, and Randolf Scholz,  “Multi-script handwritten digit recognition using multi-task learning”,  Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 355–364, 2022.
  * [6] Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, and Abiot Sinamo Boltena,  “Improving Amharic Handwritten Word Recognition Using Auxiliary Task”,  arXiv preprint arXiv:2202.12687, 2022.
  * [7] Yuqing Hu, Vincent Gripon, and Stéphane Pateux,  “Leveraging the feature distribution in transfer-based few-shot learning”,  in International Conference on Artificial Neural Networks. Springer, 2021, pp. 487–499.
  * [8] Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al.,  “Meta-dataset: A dataset of datasets for learning to learn from few examples”,  arXiv preprint arXiv:1903.03096, 2019.
  * [9] Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto,  “A baseline for few-shot image classification”,  arXiv preprint arXiv:1909.02729, 2019.
  * [10] Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang,  “A closer look at few-shot classification”,  arXiv preprint arXiv:1904.04232, 2019.
  * [11] Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum,  “The Omniglot challenge: a 3-year progress report”,  Current Opinion in Behavioral Sciences, vol. 29, pp. 97–104, 2019\.
  * [12] Chelsea Finn, Pieter Abbeel, and Sergey Levine,  “Model-agnostic meta-learning for fast adaptation of deep networks”,  in International conference on machine learning. PMLR, 2017, pp. 1126–1135.
  * [13] Alex Nichol, Joshua Achiam, and John Schulman,  “On first-order meta-learning algorithms”,  arXiv preprint arXiv:1803.02999, 2018.
  * [14] Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al.,  “Siamese neural networks for one-shot image recognition”,  in ICML deep learning workshop. Lille, 2015, vol. 2.
  * [15] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al.,  “Matching networks for one shot learning”,  Advances in neural information processing systems, vol. 29, 2016\.
  * [16] Jake Snell, Kevin Swersky, and Richard Zemel,  “Prototypical networks for few-shot learning”,  Advances in neural information processing systems, vol. 30, 2017\.
  * [17] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel,  “A simple neural attentive meta-learner”,  arXiv preprint arXiv:1707.03141, 2017.
  * [18] Pratik Mazumder, Pravendra Singh, and Vinay P Namboodiri,  “Improving few-shot learning using composite rotation based auxiliary task”,  in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 2654–2663.
  * [19] Ardhendu Shekhar Tripathi, Martin Danelljan, Luc Van Gool, and Radu Timofte,  “Few-Shot Classification By Few-Iteration Meta-Learning”,  arXiv preprint arXiv:2010.00511, 2020.
  * [20] Sachin Ravi and Hugo Larochelle,  “Optimization as a model for few-shot learning”,  2016\.
  * [21] Stanislav Fort,  “Gaussian prototypical networks for few-shot learning on omniglot”,  arXiv preprint arXiv:1708.02735, 2017.
  * [22] Etienne Bennequin,  “easyfsl”,  "https://github.com/sicara/easy-few-shot-learning".
  * [23] Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J Storkey,  “Meta-learning in neural networks: A survey”,  IEEE transactions on pattern analysis and machine intelligence, 2021\.

Mesay Samuel is a senior lecturer and PhD candidate at the faculty of
Computing and Software Engineering, Arba Minch University, Ethiopia under a
joint program [Arba Minch University-University of Hildesheim]. He received
his MSc from Jimma University, Ethiopia in Knowledge Management (2013) and his
BSc from Bahir Dar University, Ethiopia in Computer Science (2008). His
current research interests include machine learning, deep learning, optical
character recognition, expert systems and knowledge management.

Prof. Dr. Dr. Lars Schmidt-Thieme is professor for Machine Learning, heading
the Information Systems and Machine Learning Lab (ISMLL) at the Institute for
Computer Science, University of Hildesheim since 2006. Before that, he has
been assistant professor at the Institute for Computer Science at University
of Freiburg from 2003 to 2006. He graduated with a PhD in Economics and
Management with a thesis about frequent pattern mining in 2003 from University
of Karlsruhe and with a diploma (now called master) in Mathematics in 1999
from University of Heidelberg. His research interests are supervised machine
learning for complex predictors and complex decision, i.e., for all problems
whose instances cannot be described naturally by a set of attributes, i.e.,
recommender systems, relational learning problems, time series classification
etc.

Prof. Dr. Durga Prasad Sharma (DP Sharma) is associated with AMUIT MOEFDRE
under UNDP, MSRDCMAISM(RTU), & Academic Ambassador, Cloud Computing (AI), IBM,
USA. Prof. Sharma is a digital diplomat, computer scientist, strategic
innovator, and international orator. He is the recipient of 52 National and
International Awards and a wide range of appreciations including India’s one
of the highest civilian Awards “Sardar Ratna Life Time Achievements
International Award- 2015” (in memory of the first Deputy Prime Minister of
Independent India Sardar Vallabhbhai Patel). He has published more than a
dozen books (i.e., 16 Text Books & 6 distance education book series as writer
and editor) on various themes of Computer & IT and 131 International research
papers/ articles (Print & Digital) in refereed International
Journals/conferences. He has 26 years of experience in academic, research, and
professional consultancy services. He has served Mission Publiques for
Internet Governance Forum under United Nations Convention. Prof Sharma has
delivered 43 keynote speeches at numerous international conferences held in
Canada, the USA, China, South Korea, Malaysia, India, and other countries.

Dr. -Ing Abiot Sinamo is currently a Director General for ICT Sector under
Ministry of Innovation and Technology of the Federal Democratic Republic of
Ethiopia. He has got his PhD degree from Oldenburg University, Germany for the
specializations in the areas of Intelligent Systems and ERP systems. He has
assumed the post of Dean of School of Computing in Mekelle University and
delivered several courses for graduate and post graduate students for a total
of 17 years. He also advised number of MSc thesis works and is also co-
advising two PhD works. He has published several papers in the areas of
Natural Language Processing, Machine Learning, Artificial Intelligence,
Knowledge Representation, Cloud Computing, Computer Vision, Software Testing,
ERP Adoption, and etc in which he is interested to further his research
carriers.

Abey Bruck is a lecturer at the faculty of Computing and Software Engineering,
Arba Minch University, Ethiopia. He received his MSc from Addis Ababa
University, Ethiopia in Information Science (2013) and his BSc from Arba Minch
University, Ethiopia in Computer Science and IT (2008). His current research
interests include machine learning, deep learning, and robotics.

##  Appendix A The Amharic Characters

The table below shows how the Amharic characters are selected for the train,
validation, and test splits from the Amharic alphabet. This is particularly
organized to be suitable for few-shot learning research. Accordingly, 120, 61,
and 84 unique characters are identified for train, validation, and test sets
respectively. The inclusion and exclusion of characters and their families to
the specific set is done carefully so that the balance between training and
evaluation challenges could not be affected. The numbers (1-9) above the table
show the column labels and the numbers (1-34) to the left show the row labels
of the characters. However, the individual character label is given
sequentially in the Amharic alphabet from 1 to 265. Hence, the first Amharic
character for instance, has label 1 for its character, row, and column labels.
Likewise, the last character has labels 265, 34, and 7 for its character, row,
and column labels respectively.