指尖学习:创新的基于物联网的人工智能盲文学习系统

IF 3.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ghazanfar Latif, Ghassen Ben Brahim, Sherif E. Abdelhamid, Runna Alghazo, Ghadah Alhabib, Khalid Alnujaidi
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引用次数: 0

摘要

视力障碍不应妨碍个人实现自己的愿望,也不应妨碍他们为社会作出贡献。残疾人受到不公平对待的时代早已过去,如今,残疾人是社会中有生产力的成员,特别是当他们接受了正确的教育并获得了成功的正确工具时。因此,必须将最新的技术整合到能够帮助残疾人的设备和软件中。物联网(IoT)、人工智能(AI)和深度学习(ML)/深度学习(DL)是过去十年发展势头强劲的技术,可以整合起来帮助残疾人-视障人士。在本文中,我们提出了一种基于物联网的系统,可以安装在无名指上,并可以模拟视障人士的真实体验。该系统可以使用迁移学习增强的深度学习技术,学习并将阿拉伯语和英语盲文翻译成音频。该系统的开发是为了帮助视障人士及其家庭成员通过使用基于戒指的设备学习盲文,该设备使用嵌入式摄像头捕获盲文图像,识别并将其转换为音频。对捕获的盲文图像的识别是通过基于迁移学习的卷积神经网络(CNN)实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning at Your Fingertips: An Innovative IoT-Based AI-Powered Braille Learning System
Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).
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来源期刊
Applied System Innovation
Applied System Innovation Mathematics-Applied Mathematics
CiteScore
7.90
自引率
5.30%
发文量
102
审稿时长
11 weeks
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