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

Abstract

Handwriting  stroke  reflects  the  personality  and emotional    condition.    Graphology    is    scientific    method    to evaluation  personality  through  handwriting.  There  are  many features  in  graphology  to  identify  personality.  Several  previous researches  used  page  margins,  spacing,  baseline,  vertical  zone, font  size,  and  the  type  of  unique  letter  t.  Other  research  also identify the personality of signatures. This research uses feature writing  speed  and  the  type  of  letters  a,  d,  i  m,  and  t  to  identify personalities   using   structural   analysis   and   artificial   neural networks.  To  improve  accuracy,  image  writing  extracted  using wavelet transform. The system is built with the approach of the structure  and  symbol  has  been  implemented  in  software.  The results show a unique type of letter recognition by 74%, and the speed  feature  by  60%  recognition.  Variations  training  data greatly affect recognition results.