Deep learning-based iraqi banknotes classification system for blind people

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.248642

Keywords:

big data, convolutional neural network, multi-class classification, paper currency classification, Iraqi banknotes, image-to-vocal, deep learning

Abstract

Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

Author Biographies

Sohaib Rajab Awad, Ninevah University

Master of Science in Computer Engineering, Assistant Lecturer

Department of Computer and Information Engineering

College of Electronics Engineering

Baraa T. Sharef, Ahlia University

PhD, Lecturer

Department of Information Technology

College of Information Technology

Abdulkreem M. Salih, Northern Technical University

Master, Lecturer

Al-Dour Technical Institute

Fahad Layth Malallah, Ninevah University

Master, Lecturer

Department of Computer and Information Engineering

College of Electronics Engineering

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Published

2022-02-25

How to Cite

Awad, S. R., Sharef, B. T., Salih, A. M., & Malallah, F. L. (2022). Deep learning-based iraqi banknotes classification system for blind people . Eastern-European Journal of Enterprise Technologies, 1(2(115), 31–38. https://doi.org/10.15587/1729-4061.2022.248642

Issue

Vol. 1 No. 2(115) (2022): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2022 Sohaib Rajab Awad; Baraa T. Sharef, Abdulkreem M. Salih, Fahad Layth Malallah

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