Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2111
Title: Sri Lankan Currency Detector for Visually Impaired People
Authors: Abimani, R. M. K. C
Thalagahagedara, T. M. S. S. B
Thilakarathna, H. P. M. U
Wickramasingha, S. D. S. B
Nawinna, D. P
Kasthurirathna, D
Keywords: Sri Lankan Currency
Detector
Visually
Impaired People
CONVOLUTIONAL NEURAL NETWORK (CNN)
MEL – FREQUENCY CEPSTRAL COEFFICIENT(MFCC)
Issue Date: 24-Feb-2021
Publisher: Department of Computing and Information Systems, Faculty of Applied Sciences, Sabaragamuwa University of Sri Lanka
Series/Report no.: INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN COMPUTING;
Abstract: Blind people face more difficulties in day to day life. One pressing problem is they also want to use physical currency (notes and coins) as others. They always have a hard time when trying to recognize the value of a currency, we intend to address this matter by developing a mobile application for blind people. We are going to implement this currency recognition mobile application along with counting and voice command compatibility and also this application is having user-friendly interfaces, therefore easy to negotiate. By using this mobile application blind people can give voice commands to navigate and the start intended to function as a currency recognition or counting as a pleased. We are going to use the user’s mobile phone camera to get input into the app then classify the currency as a note or a coin. After that extract the features of the currency note and coin by using Convolutional Neural Network and predicting the value of the currency note and coin. This mobile application can extract the value of the coins and notes without any issue. Finally, we used Artificial Neural Network for the classification of notes and coins. Processing it and get the real value of the notes. Finally, train the Sinhala and English voice command using the CNN model and get them out as a voice
URI: http://rda.sliit.lk/handle/123456789/2111
ISSN: 2773-7136
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

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