Development of Method for Categorization on Sign Language Picture using Image Plane Adjustment Technique - Nadee, Chokemongkol; Yingkayun, Krisda (RMUTL Engineering Journal, 2020)
Most image classification is based on applying decision-making algorithms, in conjunction with image preprocessing, in order to extract distinctive features from the images and compute the most appropriate weights used in the decision making. This paper presents a method to classify sign language images by using multiple machine learning methods. The sign language images used in this work are obtained from the American Sign Language (ASL) database, which contains both black and white and color images (RGB). Experiments have shown that the multiple machine learning method is able to correctly classify sign language images with 98.06 percent accuracy.