Urdu compound Character Recognition using feed forward neural networks
Zaheer Ahmad, Jehanzeb Khan Orakzai, Inam Shamsher · 2009
Urdu compound character recognition is a scarcely developed area and requires robust techniques to develop as Urdu being a family of Arabic script is cursive, right to left in nature and characters change their shapes and sizes when they are placed at initial, middle or at the end of a word. The developed system consists of two main modules segmentation and classification. In the segmentation phase pixels strength is measured to detect words in a sentence and joints of characters in a compound/connected word for segmentation. In the next phase these segmented characters are feeded to a trained neural network for classification and recognition, where feed forward neural network is trained on 56 different classes of characters each having 100 samples. The main purpose of the system is to test the algorithm developed for segmentation of compound characters. The prototype of the system has been developed in Matlab, currently achieves 70% accuracy on the average.