Handwriting recognition using river-lake skeletonization with combination of structural elements similarity classification

This research try to apply the thinning stage using a river-lake algorithm. It has an advantage in the algorithm speed. The algorithm approach collects points which are assumed as the center point in vertical and horizontal set which every vertical point is connected to horizontal set through projection. The result of the skeleton extraction has single-pixel width which represents starting shape and is conducted in high speed process. In addition, feature extraction and classification using a structural approach, so that the classification process uses a combination of similarity of endpoints, branch points, lines, curves and rings (loops), the number and position of each character features that obtained through solving process of the endpoint, branches and junctions. Stages classification is done in three stages, namely the stage of selecting a dataset, matching features and similarity calculation. The approach taken in this research, can be prove that this method can be apply to recognition technique.