| dc.description.abstract | The education sector of Sri Lanka faced some
major conversions with online education due to the Covid-
19 pandemic and the country's economic crisis. Early
childhood education was undebated in the development
stages of the online education concept. This presented
study aims at computerizing flashcards for early
childhood education by developing an application that
helps to fulfill the fundamental foundation of education
for Sri Lankan children under the age of eight, with all
three native languages Sinhala, Tamil, and English. This
system is modelled focusing on detecting an object in an
image concerning; the specific categories (numbers,
letters, animals, fruits, vegetables) specialized for
children under the age of 8 and giving the text as well as
the audio output in all three native languages used inside
the country. The categories were selected according to
the NIE syllabus and their teaching methodologies. The
detection process is done through a set of custom-trained
models using TensorFlow and Keras. The models are built
upon CNN and YOLO algorithms. The ability to get all
three native languages are powered through the internal
translators that will map the words with the languages. A
mobile- based development through Kivy is chosen to ease
the detection process, where the user can be given the
ability of real-time detection. Each model was trained
with 80+ classes that include 100+ images with an
accuracy range from 70%-90%, which provides the user
with vast diversity and high validity. The focus on
developing this system is to introduce an online platform
for the learning process of early childhood, which is
lacking in the current Sri Lankan education system, and
teaching young children all three languages used in
means of communication inside the country while
prioritizing early childhood education in online learning
methodologies. | en_US |
