Hand alphabet is an important sign language for disability people for a long time. This communication is also necessary for a normal people to understand the meaning as well. Hand language interpretation by applying a hand image posture classification is an active research theme to solve obstacle. In this research, we propose a promising technique to apply a B spline curvature concept for supporting a triangular-based feature extraction element in a hand interpretation process. Area, inner angle and adjacent area ratio which derived from a curvature reference set are created a feature string for each alphabet posture in the template. By testing with all 24 hand alphabets, our system provides a promising result in identification satisfactorily.
