Dynamic and Competitive Skeletonization for Recognition of Decorative Characters

Thinning is one of the most important preprocessing steps in the character recognition. But this process has certain limitations like low speed and deformation. To eliminate this problem, skeletonization is used, where the character to be recognized is skeletonized. This paper describes how characters are recognized by skeletonization algorithm which is trained by neural network. Here for better understanding and experimentation, we are considering categories of decorative characters. Here, we are using an algorithm based on neural network, which determines the representative points and connections making up the skeleton by combining AVGSOM non-supervised learning. The proposed method has been applied in images with different characters and their rotations along with scaling. The results obtained are compared to existing stored database, showing quite encouraging results with more than 90% recognition efficiency. Finally, some conclusions, together with some future scopes are presented.