Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1375
Title: DNN Based Currency Recognition System for Visually Impaired in Sinhala
Authors: Gamage, C.Y.
Bogahawatte, J.R.M.
Prasadika, U.K.T.
Sumathipala, S.
Keywords: Currency Recognition
Sinhala Speech Recognition
Deep Learning Neural Network
TensorFlow
Feature Extraction
Text to Speech
Issue Date: 10-Dec-2020
Publisher: 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT
Series/Report no.: Vol.1;
Abstract: Recently researches have been conducted in the domain of currency recognition. The task of recognizing the currency notes has become challenging due to the distortion of the notes over time. Currency recognition systems in Sinhala for visually impaired people are rarely developed. To address this problem a research has been done and a relevant application has been implemented comprising three modules as Speech Recognition module, Currency Recognition module and Text to Speech Module. The major challenge in all three modules is to achieve a better accuracy using deep learning concepts. TensorFlow platform and Keras library were used to build the speech recognition neural network model for Sinhala spoken words. Deep learning neural networks were utilized for the development of currency recognition module and text to speech module.
URI: http://rda.sliit.lk/handle/123456789/1375
ISBN: 978-1-7281-8412-8
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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