Object Localization and Detecting Alphabet in Sign Language BISINDO Using Convolution Neural Network

Abstract

The BISINDO sign language is used to help deaf and mute people communicate with other people. However, not everyone is able to understand the meaning of this sign language. A system that implements artificial intelligence methods is created to solve this problem. The system uses a Convolution Neural Network algorithm with object localization techniques to detect and classify the alphabet in each form of the BISINDO finger signal. The Region Convolution Neural Network (RCNN) algorithm is used to process object localization and the CNN algorithm will perform classification process. This system is trained using 64 training data and tested using 16 test data for each type of alphabet. The results of the system testing that have been carried out are able to provide excellent accuracy values, which are above 90 percent for a training epoch of at least 50. These results produce an accuracy of 90.10% and 97.33% respectively.