Human hand detection can enable human to communicate with a machine and interact without any external device. Human hands play an important role in different applications such as medical image processing, sign language translator, gesture recognition and augmented reality. A human hand has different length and breadth for male and female. So, it is a complex articulated object consisting many connected parts and joints. Traditional methods for hand detection and tracking used color and shape information from RGB camera. Using a depth camera for hand detection and tracking is a challenging and interesting domain in computer vision. Some research has shown that using depth data for hand detection can improve human computer interaction. Recently, researchers used depth data in different hand detection and tracking methods in real time application. This paper explains different types of methods which are used for human hand detection. Various techniques and methods are explored and analyzed in this survey to determine the shortfalls and future directions in the field of hand detection from depth data.