![](https://geox.udistrital.edu.co/public/journals/4/submission_21408_20218_coverImage_en_US.png)
DOI:
https://doi.org/10.14483/23448393.21408Published:
2024-07-17Issue:
Vol. 29 No. 2 (2024): May-AugustSection:
Electrical, Electronic and Telecommunications EngineeringMobile Application for Recognizing Colombian Currency with Audio Feedback for Visually Impaired People
Aplicación móvil para el reconocimiento de moneda colombiana con retroalimentación de audio para personas con discapacidad visual
Keywords:
Mobile application, Convolutional Neural Networks, Visually Impaired People, Colombian Currency Recognition (en).Keywords:
aplicación móvil, red neuronal convolucional, personas con discapacidad visual, reconocimiento de moneda Colombiana (es).Downloads
Abstract (en)
Context: According to the census conducted by the National Department of Statistics (DANE) in 2018, 7.1% of the Colombian population has a visual disability. These people face conditions with limited autonomy, such as the handling of money. In this context, there is a need to create tools to enable the inclusion of visually impaired people in the financial sector, allowing them to make payments and withdrawals in a safe and reliable manner.
Method: This work describes the development of a mobile application called CopReader. This application enables the recognition of coins and banknotes of Colombian currency without an Internet connection, by means of convolutional neural network models. CopReader was developed to be used by visually impaired people. It takes a video or photographs, analyzes the input data, estimates the currency value, and uses audio feedback to communicate the result.
Results: To validate the functionality of CopReader, integration tests were performed. In addition, precision and recall tests were conducted, considering the YoloV5 and MobileNet architectures, obtaining 95 and 93% for the former model and 99% for the latter. Then, field tests were performed with visually impaired people, obtaining accuracy values of 96%. 90% of the users were satisfied with the application’s functionality.
Conclusions: CopReader is a useful tool for recognizing Colombian currency, helping visually impaired people gain to autonomy in handling money.
Abstract (es)
Contexto: Según el censo realizado por el Departamento Nacional de Estadística (DANE) en 2018, el 7.1 % de la población colombiana tiene una discapacidad visual. Estas personas enfrentan condiciones con autonomía limitada, como lo es el manejo de dinero. En este contexto, es necesario crear herramientas que permitan la inclusión de las personas con discapacidad visual en el sector financiero, permitiéndoles realizar pagos y retiros de manera segura y confiable.
Método: Este trabajo describe el desarrollo de una aplicación móvil llamada CopReader. Esta aplicación permite el reconocimiento de monedas y billetes de la moneda colombiana sin conexión a Internet, mediante modelos de redes neuronales convolucionales. CopReader fue desarrollada para ser utilizada por personas con discapacidad visual: toma un video o fotografías, analiza los datos de entrada, estima el valor de la moneda y utiliza retroalimentación auditiva para comunicar el resultado.
Resultados: Para validar la funcionalidad de CopReader, se realizaron pruebas de integración. Además, se llevaron a cabo pruebas de precisión y recall, considerando las arquitecturas YoloV5 y MobileNet, donde se obtuvo 95 y 93 % para el primer modelo y 99 % para el segundo. Luego, se realizaron pruebas de campo con personas visualmente discapacitadas, obteniendo valores de exactitud del 96 %. El 90 % de los usuarios quedaron satisfechos con la funcionalidad de la aplicación.
Conclusiones: CopReader es una herramienta útil para el reconocimiento de la moneda colombiana, ayudando a las personas con discapacidad visual a ganar autonomía en el manejo del dinero.
References
M. Ramamurthy and V. Lakshminarayanan, “Human vision and perception,” in Handbook of Advanced Lighting Technology, Cham: Springer International Publishing, 2015, pp. 1-23.
J. C. Suárez, “Discapacidad visual y ceguera en el adulto: revisión de tema,” Med. U.P.B., vol. 30, no. 2, pp. 170-180, 2011.
J. Alberich, D. Gómez, and A. Ferrer, Percepción Visual, 1st ed., Barcelona, Spain: Universidad Oberta de Catalunya, 2014.
D. Parra, “Los ciegos en el censo del 2018,” Instituto Nacional para Ciegos INCI, 2020. [Online]. Available: https://www.inci.gov.co/blog/los-ciegos-en-el-censo-2018
M. del R. Yepes, “La intermediación en la inclusión laboral de la población con discapacidad visual,” Instituto Nacional para Ciegos INCI2. [Online]. Available: https://www.inci.gov.co/blog/la-intermediacion-en-la-inclusion-laboral-de-la-poblacion-con-discapacidad-visual
Banco de la República, “Marca táctil en los billetes,” 2016. [Online]. Available: https://www.banrep.gov.co/es/node/31529
Orcam, “OrCam MyEye,” 2023. [Online]. Available: https://www.orcam.com/en-us/myeye-store
M. Doudera, “Cash reader: Bill identifier,” mobile app, Google Play, 2024. https://play.google.com/store/apps/details?id=com.martindoudera.cashreader&hl=en&gl=US
C. Aramendiz U., D. Escorcia G., J. Romero C., K. Torres R., and C. Triana P., “Sistema basado en reconocimiento de objetos para el apoyo a personas con discapacidad visual (¿Que tengo enfrente?),” Investig. y Desarro. en TIC, vol. 11, no. 2, pp. 75-82, 2020. https://revistas.unisimon.edu.co/index.php/identic/issue/view/261
S. Vaidya, N. Shah, N. Shah, and R. Shankarmani, “Real-time object detection for visually challenged people,” in 2020 4th Int. Conf. Intell. Com. Control Syst. (ICICCS), 2020, pp. 311-316. https://doi.org/10.1109/ICICCS48265.2020.9121085
M. Awad, J. El Haddad, E. Khneisser, T. Mahmoud, E. Yaacoub, and M. Malli, “Intelligent eye: A mobile application for assisting blind people,” in 2018 IEEE Middle East North Africa Comm. Conf. (MENACOMM), 2018, pp. 1-6. https://doi.org/10.1109/MENACOMM.2018.8371005
J. Tamayo, “Sistema de reconocimiento de billetes para personas con discapacidad visual mediante visión artificial,” undergraduate thesis, EIA University, 2018. https://repository.eia.edu.co/entities/publication/321a4983-afec-4865-9915-4ede5b26a435
J.-Y. Lin, C.-L. Chiang, M.-J. Wu, C.-C. Yao, and M.-C. Chen, “Smart glasses application system for visually impaired people based on deep learning,” in 2020 Indo – Taiwan 2nd Int. Conf. Comp. Analytics Net. (Indo-Taiwan ICAN), 2020, pp. 202-206. https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181366
O. Stephen, Y. J. Jang, T. S. Yun, and M. Sain, “Depth-wise based convolutional neural network for street imagery digit number classification,” in 2019 IEEE Int. Conf. Comp. Sci. Eng. (CSE) and IEEE Int. Conf. Embedded Ubiq. Comp. (EUC), 2019, pp. 133-137. https://doi.org/10.1109/CSE/EUC.2019.00034
Joshua, J. Hendryli, and D. E. Herwindiati, “Automatic License Plate Recognition for Parking System using Convolutional Neural Networks,” in 2020 Int. Conf. Inf. Manag. Technol. (ICIMTech), 2020, pp. 71-74. https://doi.org/10.1109/ICIMTech50083.2020.9211173
S. M. M. Roomi and R. B. J. Rajee, “Coin detection and recognition using neural networks,” in 2015 Int. Conf. Circuits, Power Comput. Technol. [ICCPCT-2015], 2015, pp. 1-6. https://doi.org/10.1109/ICCPCT.2015.7159434
N. Capece, U. Erra, and A. V. Ciliberto, “Implementation of a Coin Recognition System for Mobile Devices with Deep Learning,” in 2016 12th Int. Conf. Signal-Image Technol. Internet-Based Syst. (SITIS), 2016, pp. 186-192. https://doi.org/10.1109/SITIS.2016.37
J. Xu, G. Yang, Y. Liu, and J. Zhong, “Coin Recognition Method Based on SIFT Algorithm,” in 2017 4th Int. Conf. Inf. Sci. Control Eng. (ICISCE), 2017, pp. 229-233. https://doi.org/10.1109/ICISCE.2017.57
S. Mittal and S. Mittal, “Indian Banknote Recognition using Convolutional Neural Network,” in 2018 3rd Int. Conf. Internet Things: Smart Innov. Usages (IoT-SIU), 2018, pp. 1-6. https://doi.org/10.1109/IoT-SIU.2018.8519888
A. U. Tajane, J. M. Patil, A. S. Shahane, P. A. Dhulekar, S. T. Gandhe, and G. M. Phade, “Deep Learning Based Indian Currency Coin Recognition,” in 2018 Int. Conf. Adv. Commun. Comput. Technol. (ICACCT), 2018, pp. 130-134. https://doi.org/10.1109/ICACCT.2018.8529467
N. A. J. Sufri, N. A. Rahmad, N. F. Ghazali, N. Shahar, and M. A. As’ari, “Vision Based System for Banknote Recognition Using Different Machine Learning and Deep Learning Approach,” in 2019 IEEE 10th Control Syst. Grad. Res. Colloq. (ICSGRC), 2019, pp. 5-8. https://doi.org/10.1109/ICSGRC.2019.8837068
U. R. Chowdhury, S. Jana, and R. Parekh, “Automated System for Indian Banknote Recognition using Image Processing and Deep Learning,” in 2020 Int. Conf. Comput. Sci., Eng. Appl. (ICCSEA), 2020, pp. 1-5. https://doi.org/10.1109/ICCSEA49143.2020.9132850
R. Tasnim, S. T. Pritha, A. Das, and A. Dey, “Bangladeshi Banknote Recognition in Real-time using Convolutional Neural Network for Visually Impaired People,” in 2021 2nd Int. Conf. Robotics, Electr. Signal Process. Tech. (ICREST), 2021, pp. 388-393. https://doi.org/10.1109/ICREST51555.2021.9331182
Google Play, “Android Accessibility Suite,” Talkback, 2023. [Online]. Available: https://play.google.com/store/apps/details?id=com.google.android.marvin.talkback&hl=en_US&gl=US
B. Dwyer, J. Nelson, and J. Solawetz, “Software Roboflow (Version 1.0).” Roboflow Inc., 2022. https://roboflow.com/
E. Bisong, “Google colaboratory,” in Building Machine Learning and Deep Learning Models on Google Cloud Platform, Berkeley, CA: Apress, 2019, pp. 59–64.
Taryana Suryana, “Rational Unified Process (RUP),” Ration. Unified Process, vol. 3, no. September, pp. 1-6, 2007.
D. Trenkler, “A handbook of small data sets: Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J. & Ostrowski, E. (1994): Chapman & Hall, London. xvi + 458 pages, including one diskette with data files (MS-DOS), 40 British Pounds, ISBN 0-412-39920-2,” Comput. Stat. Data Anal., vol. 19, no. 1, p. 101, Jan. 1995.
Wikipedia, “System usability scale,” 2024. [Online]. Available: https://en.wikipedia.org/wiki/System_usability_scale
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
License
Copyright (c) 2024 Camila Bolaños-Fernández, Eval Bladimir Bacca-Cortes
![Creative Commons License](http://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
From the edition of the V23N3 of year 2018 forward, the Creative Commons License "Attribution-Non-Commercial - No Derivative Works " is changed to the following:
Attribution - Non-Commercial - Share the same: this license allows others to distribute, remix, retouch, and create from your work in a non-commercial way, as long as they give you credit and license their new creations under the same conditions.