Handwriting recognition using a combination of structural elements similarity

Today, good performance of handwriting recognition system has high complexity or complex computation especially in training and classification. We have developed an offline handwriting recognition system with structural approach. Each character through the stages of pre-processing, structural feature extraction and classification process using a combination of similarity endpoint, branch, line and curve, loop, number and position of each feature obtained from the endpoint and branch. This research focuses on feature extraction stage and classification process. Classification process performed using three stages: selection of dataset, mounting features and calculation similarity. Because of acquisition process of handwriting were performed using offline method, then confounding elements becomes very high. The approach taken in this research can be improved its level accuracy of detection digit number to 89,80%, capital letters 86,60% and normal letter 84,92%.