Online Urdu Handwritten Character Recognition: Initial Half Form Single Stroke Characters

Necessity of unfolding the enticing field of handwritten character recognition is revealed with the mushroom growth of portable devices. Effective human machine interaction insists the development of a reliable and efficient online handwritten character recognition system. The quest becomes more challenging when it involves Urdu script based languages especially written in Nastalique font. Urdu, in Nastalique style, is a context sensitive and a highly cursive language. Difficulty arises in this style of writing as the shape of a character depends whether it is written in isolated, initial, medial or terminal position in a word. In this paper, online recognition of Urdu characters in their initial half form have been studied. Data is collected using a pen-tablet and the signal is stored as a binary file containing x & y coordinates and pressure values rather than in an image form to reduce complexity of the recognition problem. Wavelets transform is applied to analyze the character signal. Back propagation neural network classifier for single stroke characters in initial half form is designed with overall accuracy of 91.3%.