Abstract
A human–computer interaction is generally limited to taking input from the user using handheld devices like keyboard, mouse, or scanners. With the advancement in computers, the user interaction approaches have also advanced. Direct use of hands as an input device is an attractive method for providing natural Human–Computer Interaction. It is also helpful for people who use sign language. The chapter aims to study the existing methods for Hand Gesture Recognition and provide a comparative analysis of the same. The entire process of hand gesture recognition is divided into three phases: hand detection, hand tracking, and recognition. The chapter includes a review of the different methods used for the hand gesture recognition. The recognition phase is classified based on the way the input is received as glove based or vision based. For recognition, various methods like Feature extraction, Hidden Markov Model (HMM), Principal Component Analysis (PCA) are compared along with the reported accuracy.
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