American Static Signs Recognition Using Leap Motion Sensor
Rajesh B. Mapari, Govind Kharat · 2016
Advancement in technology has opened new ways in the field of Human Machine Interaction. A Novel method for recognition of American Sign Language (ASL) is proposed using Leap Motion Sensor. Static signs (A to Z and numbers from 1 to 10) excluding J and Z are used for processing. However 2 and 6 also excluded from dataset as the posture of these is similar to V and W respectively. Features Set consist of positional values (fingers and palm), distance and angle values. Total 48 features are used to recognize ASL using Multilayer Perceptron (MLP) which is a feed forward artificial neural network. Dataset consists of 146 users who have performed 32 signs resulting in total dataset of 4672 signs. Out of this 90% dataset is used for training and 10% dataset is used for CV (Cross Validation)/testing. The average classification accuracy obtained is near about 90%.