Methodology and comparison of devices for recognition of sign language characters

B. Silva, G. Furriel, Wesley Calixto Pacheco, Júnio S. Bulhões
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引用次数: 3

Abstract

The purpose of this work is to develop devices capable of identifying sign language characters and comparing them in order to verify layout with better accuracy and robustness. The recognition is performed using Artificial Neural Networks and all the input data are signals from flex sensors, accelerometers and gyroscopes, positioned differently on each device. After being trained, validated and tested, the network reachs hit rate about 95.8%. It is proposed as solution to deaf people's accessibility and presents as layout proposal for the development of new devices for recognition of signals that express complete words and phrases.
手语字符识别方法及设备比较
本工作的目的是开发能够识别和比较手语字符的设备,以更好的准确性和鲁棒性来验证布局。识别是通过人工神经网络完成的,所有输入数据都是来自伸缩传感器、加速度计和陀螺仪的信号,这些传感器在每个设备上的位置不同。经过训练、验证和测试,网络命中率达到95.8%左右。本文提出了一种针对聋人无障碍的解决方案,并提出了一种用于识别表达完整单词和短语的信号的新设备的设计方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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