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首页> 外文期刊>International Journal of Information and Communication Sciences >Bridging Communication Gap Among People with Hearing Impairment: An Application of Image Processing and Artificial Neural Network
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Bridging Communication Gap Among People with Hearing Impairment: An Application of Image Processing and Artificial Neural Network

机译：弥合听力障碍人士之间的沟通鸿沟：图像处理和人工神经网络的应用

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Before the present study, no sign language recognition system for the Nigeria indigenous sign language particularly Yoruba language has been developed. As a result, this research endeavors at introducing a Yoruba Sign Language recognition system using image processing and Artificial Neural Network (ANN).The proposed system (YSLRS) was implemented and tested. 600 images from 60 different signers were gathered. The images were acquired using vision based method, the different signers were asked to stand in front of a laptop's camera make sign number from one to ten with their fingers in three different times and the images were stored in a folder. The image dataset was pre-processed for proper presentation for de-noising, segmentation and feature extraction. Thereafter, pattern recognition was done using feed forward back propagation ANN. The study revealed that Median filter with higher PSNR of 47.7 a lower MSE of 1.11, performed better than the Gaussian filter. Furthermore, the efficiency of the developed system was determined using mean square error and the best validation performance occurred at 25 epochs with a MSE of 0.004052, implying than ANN was able to adequately recognize the pattern of the Yoruba signs. Histogram was also used to determine the efficiency of the system, it can be seen that the histogram of the trained, tested and validated error bars were close to zero error, implying that the ANN and Receiver Operating Characteristic (ROC) was used to evaluate the performance of ANN in matching the features of the Yoruba Signs, which shows that ANN performed efficiently, having a high true positive rate and a minimum false positive rate. Finally, YSLRS developed in the study would reduce negative attitudes of victimizations suffered by the hearing-impaired individuals, by bridging communication gap among Nigerian PWD with hearing impairment.
机译：在本研究之前，尚未开发针对尼日利亚土著手语特别是约鲁巴语的手语识别系统。因此，本研究致力于引入一种使用图像处理和人工神经网络（ANN）的约鲁巴语手语识别系统，并对该系统（YSLRS）进行了实施和测试。收集了来自60个不同签名者的600张图像。使用基于视觉的方法获取图像，要求不同的签名者在三个不同的时间里用手指站在笔记本电脑的摄像机前，用手指从1到10签名号，并将图像存储在文件夹中。对图像数据集进行了预处理，以进行适当的演示，以进行降噪，分割和特征提取。此后，使用前馈传播ANN进行模式识别。研究表明，具有较高PSNR的中值滤波器为47.7，较低的MSE为1.11，其性能优于高斯滤波器。此外，使用均方误差确定开发系统的效率，最佳验证性能出现在25个纪元处，MSE为0.004052，这意味着ANN能够充分识别约鲁巴符号的模式。直方图还用于确定系统的效率，可以看出，经过训练，测试和验证的误差线的直方图接近零误差，这意味着使用了ANN和接收器工作特性（ROC）来评估系统的效率。人工神经网络在匹配约鲁巴标志的特征方面的性能，这表明人工神经网络的执行效率很高，具有很高的真实阳性率和最低的假阳性率。最后，该研究开发的YSLRS通过弥合尼日利亚听力障碍人士与残疾人之间的沟通鸿沟，将减少听力受损人士遭受伤害的负面态度。

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