Abstract:
The transformation of currency notes and coins denomination recognition to an automated
system as a solution for visually debilitated individuals to overcome the difficulties facing
when handling monetary transactions. This research presents a model to detect currency
notes and coins to visually debilitated individuals and to retrieve the current monetary
position of them as per their obligation and provide audio output in the Sinhala language.
The general procedure of the system includes digital image processing, convolutional
neural network, voice identification algorithm, and monetary position calculation
algorithm. Sri Lanka currency notes and coins images were captured in a wide variety of
environments, in association with lighting conditions and background to make the data
set, using the image preprocessing technique. The YOLOv2, R-CNN network model
which is a high speed, real-time object detection algorithm to verify objects as currency
notes and coins. Then by using Keras Xception model, predict images, feature extraction
and fine-tuning have been done to train the data set. The Computer vision used to
improve machine perception to retrieve real-time detection. The detected currency notes
or coins denomination is provided as an audio output, then retrieves the obligation of the
user, which is whether to debit, credit or to retrieve the current monetary position. The
monetary position provides audio output in the virtual wallet as a substitute for a realworld wallet since impairments have a scarcity in memorizing their actual balance. The
study revealed a system to detect and retrieve the currency denomination and monetary
position of blind individuals with the overall accuracy rate of 100% in algorithm
experiments.
Keywords: Visually debilitated individuals, Currency recognition, Virtual wallet,
monetary position
