Abstract

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.

Highlights

  • There is big communication gap between the hearing and the hearing-impaired people

  • It could be further seen that the best validation performance occurred at 25 epochs with a Mean Square Error (MSE) of 0.004052, implying than Artificial Neural Network (ANN) was able to adequately recognize the pattern of the Yoruba signs

  • All plot shows that true positive rate was high, while the minimum false positive rate computed shows that developed ANN model was able to recognize the Yoruba Sign Language effectively

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Summary

Introduction

There is big communication gap between the hearing and the hearing-impaired people. Bridging this communication gap is a big task and requires technological interventions. A lot of research works have been done in the area of sign language recognition system development to provide solution to communication problems within the Deaf community. Many researchers have endeavored to come-up with the optimum algorithm or solution for 100 percent recognition of signs. A large numbers of techniques are being developed in order to recognize and classify the gestures of various sign languages; for instance, Arabic Sign Language [2] and [6], Tamil sign language [11] Chinese sign language [12], American sign language [1] Mexican sign language [10], Albanian sign language [4] and Korean sign language [7]

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