Static Sign Language Recognition Using Deep Learning
Lean Karlo S. Tolentino, Ronnie O. Serfa Juan, August C. Thio-ac, Maria Abigail B. Pamahoy, Joni Rose R. Forteza, Xavier Jet O. Garcia · International Journal of Machine Learning and Computing · 2019
A system was developed that will serve as a learning tool for starters in sign language that involves hand detection.This system is based on a skin-color modeling technique, i.e., explicit skin-color space thresholding.The skin-color range is predetermined that will extract pixels (hand) from non-pixels (background).The images were fed into the model called the Convolutional Neural Network (CNN) for classification of images.Keras was used for training of images.Provided with proper lighting condition and a uniform background, the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition, 93.44% for number recognition and 97.52% for static word recognition, thus surpassing that of other related studies.The approach is used for fast computation and is done in real time.