Parallel selection of multi-category features for online handwritten character recognition

Online handwritten recognition is gaining more interest due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. The large number of writing styles and the variability between them makes the handwriting recognition problem a very challenging area for researchers. Many previous efforts have utilized many different approaches for recognition in online handwriting using various ANN classifier-modeling techniques. Different types of feature extraction techniques have also been used. It has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and a necessary number of samples. A common problem with the multi-category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solution the search space can be divided based on an individual category in each sub region and finally merging them through decision spport system. In this paper we propose a canonical GA based modular feature selection approach combined with standard MLP for multi category feature selection in online handwriting recognition.