Abstract:
Sinhala is a unique and national language spoken only in Sri Lanka. Sinhala
characters are difficult to recognize in images with complex backgrounds, and
some visually impaired people cannot read/write properly due to their eye
problems. This study is mainly focused on extracting and recognizing Sinhala
text from images with complex backgrounds using a Convolutional neural
network. Different 33 Sinhala characters (10 images per each character) are
used as a training dataset. 400 images of bus destination name boards with
Sinhala characters are collected as a testing dataset. The character recognition
model is trained using CNN with the collected training dataset. The model is
trained 10 times until an accuracy of 81% is achieved. The collected dataset
of bus destination name board images is used to extract and recognize Sinhala
characters and pre-processing is performed on them to check the availability
of text in the images. After the recognition process, the non-text regions are
removed. Three types of segmentation such as line, word, and character are
performed on preprocessed images to segment each character on the image.
The segmented characters are used as input for the recognition model, and it
is successfully identified as Sinhala characters. Furthermore, there are
numerous types of Sinhala optical character recognition and Sinhala
handwritten character recognition research, however, there is no research on
recognizing Sinhala text on images with complex backgrounds. Moreover,
people with eye problems/issues can easily read Sinhala characters in images
with a complex background as an important benefit of this proposed
methodology.
