Convolutional Neural Network Implementation in BISINDO Alphabet Sign Language Recognition System
Abstract
This research develops a system for recognizing finger spelling gestures in Indonesian Sign Language (BISINDO) using Convolutional Neural Network (CNN). The objective of this research is to apply the Convolutional Neural Network (CNN) method to the BISINDO finger spelling gesture recognition system to improve its accuracy. The method employed is Convolutional Neural Network (CNN), an effective method for processing image data for pattern recognition. Based on the test results, the system demonstrates that the developed CNN model is capable of recognizing BISINDO finger spelling gestures with an accuracy of 97.5%. This indicates that the BISINDO finger spelling gesture recognition system performs well in pattern recognition. The implementation of the system for real-time prediction via a web interface using Flask also enhances its accessibility. However, there is still room for improvement, particularly in recognizing one of the 26 letters that has not been predicted accurately. For further development, it is recommended to consider collecting a larger dataset and incorporating more complex gesture variations to improve recognition accuracy.
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