GEOMETRIC SHAPE-RECOGNITION WITH FUZZY FILTERED INPUT TO A BACKPROPAGATION NEURAL-NETWORK

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in hand-held computers. Although it is possible to store and retrieve drawings through the use of electronic ink, further manipulation of these drawings require recognition to be performed. In this paper, we propose a new approach to invariant geometric shape recognition which utilizes a fuzzy function to reduce noise and a neural network for classification. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perception of the shape. Our application's aim is to recognize and correctively redraw hand drawn ellipses, circles, rectangles, squares and triangles. The neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape is sufficient. The results are very successful, such that the neural network correctly classified shapes which were not included in the training set.