Currency Detection for Visually Impaired Iraqi Banknote as a Study Case

Raghad Raied Mahmood Et Al

doi:10.17762/turcomat.v12i6.6078

Abstract

It is relatively simple for a normal human to interpret and understand every banknote, but one of the major problems for visually impaired people are money recognition, especially for paper currency. Since money plays such an important role in our everyday lives and is required for every business transaction, real-time detection and recognition of banknotes become a necessity for blind or visually impaired people For that purpose, we propose a real-time object detection system to help visually impaired people in their daily business transactions. Dataset Images of the Iraqi banknote category are collected in different conditions initially and then, these images are augmented with different geometric transformations, to make the system strong. These augmented images are then annotated manually using the "LabelImg" program, from which training sets and validation image sets are prepared. We will use YOLOv3 real-time Object Detection algorithm trained on custom Iraqi banknote dataset for detection and recognition of banknotes. Then the label of the banknotes is identified and then converted into audio by using Google Text to Speech (gTTS), which will be the expected output. The performance of the trained model is evaluated on a test dataset and real-time live video. The test results demonstrate that the proposed method can detect and recognize Iraqi paper money with high mAP reaches 97.405% and a short time.

Highlights

According to the study conducted worldwide by the World Health Organization (WHO), about 285 million people suffer visually impaired, of whom 39 million were blind, 246 million had low vision
Previous research has suggested many strategies to overcome the issues of visually impaired people (VIPs) to interpret and understand every banknote, but the proposed ideas of these strategies are generally high in complexity, and not cost-effective etc.[4]
The testing results have proved the power of training, variation in dataset and YOLOv3 model to detect and recognize Iraqi banknote

Summary

Introduction

According to the study conducted worldwide by the World Health Organization (WHO), about 285 million people suffer visually impaired, of whom 39 million were blind, 246 million had low vision. The abilities of the visually impaired to interpret and understand every banknote are limited or influenced. Previous research has suggested many strategies to overcome the issues of visually impaired people (VIPs) to interpret and understand every banknote, but the proposed ideas of these strategies are generally high in complexity, and not cost-effective etc.[4]. Every country has its own money, which comes in a variety of colors, sizes, shapes, and patterns It is very complicated for anyone who is visually impaired to recognize and count various types of money. The camera will capture the banknote's image that is in front of the person Thereafter, it gets processed using deep learning methods and, in turn, the output, which is the name of the banknote, will be converted into audio for the user through the audio jack. A system is proposed to aid Iraqi visually impaired people in dealing with dayto-day business activities

Related Work

Image Augmentation and Annotation

Experiments And Analysis

Conclusion