Identification of Speed and Unique Letter of Handwriting Using Wavelet and Neural Networks

Abstract

Graphology is scientific method to evaluation personality and emotion condition through handwriting and signature. There are many features to identify personality so that previous researches made handwriting analysis automatically. There are page margins, spacing, baseline, vertical zone, font size, slant, pen pressure, and the type t letter. While other studies used features of signature. As image, the analysis of graphology is divided into two approaches that graphics features and segmentation digit each character. This research integrated both of approach to identify personality of handwriting. It used speed and the type of a, d, i m, t letters as features using structure analysis and artificial neural networks. Type of letter recognition was done after character segmentation. Wavelet transform was used to improve recognition. The proposed methods could be used to identify personality of handwriting. Identification of speed feature using structure analysis toward page margin, spacing between lines, and spacing between words that gave 81% accuracy. While identification of unique letters using neural network with multilayer perceptron architecture, which gave 74% accuracy. Variations training data greatly affect recognition.