Sign language word translator using Neural Networks for the Aurally Impaired as a tool for communication

Jessie R. Balbin, Dionis A. Padilla, F. Caluyo, Janette C. Fausto, Carlos C. Hortinela, C. O. Manlises, Christine Kate S. Bernardino, Ezra G. Fiñones, Lanuelle T. Ventura
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引用次数: 18

Abstract

Kohonen Self Organizing Maps are a type of Neural Networks which learn to recognize patterns and classify data sets in unsupervised manner. This research aimed to develop a system which converts hand gestures into Filipino words using algorithm such as Kohonen Self-Organizing Map. The system uses a webcam to capture hand images which can be processed to serve as input for Self-Organizing Map. Image processing techniques such as: color segmentation, visual-hand tracking, pre-processing, and feature extraction are used to achieve the objective. The system was developed using neural network toolbox and graphical user interface in MATLAB. The results show that the system can achieve 97.6% of recognition rate for 5 persons. It can be concluded that the system can be used by different users while generating high recognition rate.
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手语单词翻译使用听觉受损的神经网络作为沟通工具
Kohonen自组织图是一种神经网络,它学习以无监督的方式识别模式和分类数据集。本研究旨在开发一个系统,该系统使用Kohonen自组织地图等算法将手势转换为菲律宾语。该系统使用网络摄像头捕捉手部图像,这些图像可以作为自组织地图的输入。图像处理技术,如:颜色分割,视觉手跟踪,预处理和特征提取是用来实现目标。系统采用MATLAB中的神经网络工具箱和图形用户界面进行开发。结果表明,该系统对5个人的识别率达到97.6%。可以得出结论,该系统可以被不同的用户使用,同时产生较高的识别率。
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