Neural Network based Clustering using Visual Features of Characters’ Shape in Image

Clustering gathers similar objects. A Character can also be treated as object and can be recognized in the image through its visual features. In this work, characters of the Urdu script are clustered on the basis of 18 different visual features. A Kohonen Self Organizing Map is used for clustering with four different topologies of sizes 6x5, 8x7, 9x8, and 10x10. Each topology is checked under 75, 100, 150 and 200 numbers of epochs. 30 Urdu characters make 106 different shapes due to the four different positions in the word. These 106 shapes are then classified into 53 general classes based on graphical similarity. The shape of each class comprises features for its description. Considering only 18 features of each shape, 53 general classes are then grouped into clusters using a Kohonen Self Organizing Map (K-SOM). The above mentioned work has been implemented in MATLAB.