BISINDO Sign Letters Recognition Through HOG Features and Bagging Decision Tree - Indra, Dolly; Alwi, Erick Irawadi; Anwar, Faudiah; Prihandani, St. Nadya Kurnia (Jurnal Ilmiah Informatika Komputer, 2025)
Sign language is one of the primary means of communication for people with hearing disabilities. BISINDO (Indonesian sign language) communicates using hand movements, among other things. One solution to this problem is to use image processing to recognize BISINDO letters A-Z based on hand movements. This study aims to create a BISINDO letter recognition system based on image processing using several stages, namely, preprocessing such as converting RGB images to grayscale images, then improving image quality by adjusting image contrast and removing noise with a median filter, HOG (Histogram of Oriented Gradients) feature extraction, and Bagging Decision Tree classification. A total of 156 images were used in the dataset, consisting of 104 letter images for training data and 52 letter images for test data. The data will be processed in the system as training data, and the dataset will then be stored in ‘mat’ format. Based on the results of testing Classification using Bagging Decision Tree, which produced an average accuracy rate of 86.5%. Thus, this research is expected to contribute to the development of BISINDO character recognition technology based on digital image processing.