Design concept of sign language recognition translation and gesture recognition control system based on deep learning and machine vision

Yiyang Zhang, X. Pu, Xiaolu Wang, Haopeng Guo, Ke Liu, Qian-Ying Yang, Lili Wang
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Abstract

With the development of society, gestures are used in many aspects, but the computer's functionality for gesture recognition is still to be improved. This article is mainly a preliminary idea of a basic gesture recognition system built based on the existing Google deep learning framework TensorFlow and gesture recognition components in MediaPipe and OpenCv machine vision open-source library. The training dataset is first subjected to skeleton key point coordinate extraction, then the pre-processed dataset is used to train the neural network and constitute the preliminary model, and finally the model is corrected and changed in the end.
基于深度学习和机器视觉的手语识别翻译与手势识别控制系统的设计理念
随着社会的发展,手势在很多方面都有应用,但是计算机对手势识别的功能还有待提高。本文主要是基于现有的Google深度学习框架TensorFlow以及MediaPipe和OpenCv机器视觉开源库中的手势识别组件构建一个基本的手势识别系统的初步构想。首先对训练数据集进行骨架关键点坐标提取,然后使用预处理后的数据集对神经网络进行训练并构成初步模型,最后对模型进行修正和更改。
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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