Hand Contour Recognition In Language Signs Codes Using Shape Based Hand Gestures Methods

Ade Silvia, Nyayu Latifah Husni


The deaf and speech impaired are loosing of hearing ability followed by disability of developing talking skill in everyday communication.  Disability of making normal communication makes the deaf and speech impaired being difficult to be accepted by major normal community.  Communication used is gesture language, by using hand gesture communication. The weakness of this communication is that misunderstanding and limitation, it’s due to hand gesture is only understood by minor group.  To make effective communication in real time, it’s needed two ways communication that can change the code of hand gesture pattern to the texts and sounds that can be understood by other people. In this research, it’s focused on hand gesture recognition using shaped based hand algorithm where this method classifies image based on hand contour using hausdorff and Euclidian distance to determine the similarity between two hands based on the shortest range.  The result of this research is recognizing 26 letters gesture, the accuracy of this Gesture is 85%, from different human hands, taken from different session with different lighting condition and different range of camera from image.  It also can recognize 70% different hand contour.  The different of this research from other researches is the more the objects are, the less the classification of hands size is. Using this method, hands size can be minimized.

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Faculty of Computer Science | Sriwijaya University