Abstract:
In order to facilitate interactions between people and computers, technology has been enhanced to
recognize both printed and handwritten materials. But there is still a lot of debate over Asian
languages. Since Sri Lanka is the one country in Asia that speaks Sinhala just like its official
language, character recognition for that language is currently a work in progress.
Sinhala characters differ from those in other widely spoken languages by having linkages between
characters in a circular, complicated pattern. The Sinhala language uses 60 fundamental characters
(non-cursive). A Sinhala character set is further improved by modifiers that can be applied to basic
characters. As a result, it can be challenging to identify Sinhala handwritten characters.
Most character detection and segmentation research efforts employ patterns match and image
processing approaches. On the other hands, these approaches are unable to adapt to the changes.
This study aims to develop a handwritten Sinhala character identification system using
convolutional neural networks (CNN). The main objective of this study is to create a system that
can accurately and reliably recognize Sinhala handwriting using a combination of classification
and segmentation techniques. This study generated a dataset of 200 samples each of the 31
character classes. This dataset was used to train a CNN model, which has a 98 percent overall
accuracy rate.
Key Words: Deep Learning, Sinhala Handwritten Character Recognition, Convolutional Neural
Network, and Image Processin
