Sign Language to Voice Translator Using Tensorflow and TTS Algorithm

G. K, Akkash. C, Sagaya Selvaraj. A, Arockiya Rayal Ruffus. M, Cesario De Cruz. E
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Abstract

In this system, it helps to convert the sign language to voice with hand gestures understanding and capture the motion of hands. Many sign languages are natural languages, but they are differing in construction from oral languages used in proximity to them, and are employed mainly by deaf people in order to communicate. It is based on a Raspberry Pi with a camera module and is programmed in Python with the Open-Source Computer Vision (Open CV) library as a backend. A built-in image processing algorithm on the Raspberry Pi is named gesture that tracks an object (a finger) with features pulled out. The main purpose of a gesture recognition system is to establish a connection between a human and a computer control system. Camera is used in this system and it captures the various gestures of hands. Various algorithms are taking place in processing of image. First preprocessing of the image takes place. Finally, the sign is identified by using Tensor flow algorithm and the outcome result as a voice using TTS algorithm. Open CV Python is implemented in this system. Various libraries are used in this system.
基于Tensorflow和TTS算法的手语语音翻译
在这个系统中,它有助于将手语转换为语音,手势理解和捕捉手的动作。许多手语都是自然语言,但它们在结构上与附近使用的口语不同,主要是聋哑人用来交流的。它基于带有摄像头模块的树莓派,并使用Python编程,使用开源计算机视觉(Open CV)库作为后端。树莓派上的内置图像处理算法被命名为gesture,它可以跟踪物体(手指)的特征。手势识别系统的主要目的是建立人与计算机控制系统之间的联系。在这个系统中使用了摄像头,它可以捕捉到各种手势。在图像处理中出现了各种各样的算法。首先对图像进行预处理。最后,使用张量流算法对符号进行识别,并使用TTS算法将结果作为语音。本系统实现了Open CV Python。本系统使用了多种库。
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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