With the advance of technology the use of human computer interaction (HCI) has improved day by day. Computer vision plays an important role to provide information to design more simple and efficient approaches for HCI. The proposed approach uses skin color model to identify the hand from the image, and further preprocessing is done in order to remove unwanted noise and areas. Blob analysis is done in-order to extract the hand gesture from the image considering that the largest blob is the hand. Then the blob is resized into a standard size in order to eliminate size variant constraint. Sampling of the boundary line of the hand gesture is done by overlapping grid lines and extracting the point of intersection of the grid line and the boundary. Freeman chain code is used to represent the boundary of the hand gesture. In order to minimize the length of chain code run-length encoding is done. Finding the first difference of the chain code its shape number is obtained. Shape number can be used to identify each of the gesture uniquely.

Human Computer Interaction, Computer Vision, Static Gesture, Nepali Sign Language, Blob, Freeman Chain Code, Shape Number

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