基于深度神经网络的僧伽罗语视障货币识别系统

C. Y. Gamage, J. R. M. Bogahawatte, U. Prasadika, S. Sumathipala
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引用次数: 2

摘要

近年来,人们在货币识别领域进行了大量的研究。随着时间的推移,由于纸币的扭曲,识别纸币的任务变得具有挑战性。僧伽罗语为视障人士开发的货币识别系统很少。针对这一问题,本文进行了研究并实现了相应的应用程序,包括语音识别模块、货币识别模块和文本转语音模块三个模块。这三个模块的主要挑战是使用深度学习概念实现更好的准确性。利用TensorFlow平台和Keras库构建僧伽罗语口语语音识别神经网络模型。利用深度学习神经网络开发了货币识别模块和文本转语音模块。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DNN based Currency Recognition System for Visually Impaired in Sinhala
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.
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