A Comparative Study on Performance of SVM and CNN in Tanzania Sign Language Translation Using Image Recognition

Sign language is an effective form of communication for speech impaired people. However, there is a challenge for people without impairment to communicate with speech impaired people because most are unaware of the language. There are several Machine Learning techniques that have been used in sign language translation. However, no study has been found in Tanzania Sign Language which is the sign language used by speech impaired people in Tanzania. This study seeks to compare the performance of SVM and CNN on translating sign language through the image recognition. The study employs Tanzanian Sign Language images as datasets. Principal Component Analysis was employed for feature extraction. Furthermore, the study used Combined 5x2cv F test to compare the two techniques. The findings indicate that CNN scored 96% in all of the parameters which are accuracy, recall, and precision while SVM scored similar rate in precision but lag behind on recall and accuracy. Additionally, the results show that there is significant difference in performance between the techniques. Therefore, the study recommends the use of CNN since it has high accuracy.