Multifont Ottoman character recognition using support vector machine

In this study, an Optical Character Recognition (OCR) system, which implements segmentation, normalization, edge detection and recognition of the Ottoman script, is proposed. Each multifont Ottoman character is written with four different shapes according to its position in the word being at beginning, middle, at the end and in isolated form. We have used printed type of Ottoman scripts in image acquisition. Then image segmentation, normalization and finally edge detection are performed for feature extraction, where edge detection is achieved by Cellular Neural Network (CNN) approach. After these pre-proces steps, we recognize these multifont Ottoman characters using Support Vector Machine (SVM) technique. In SVM training, polynomial (linear and quadratic) and Gaussian Radial Basis Function kernels are chosen. The proposed recognition system has succeeded in classification up to 87.32% with quadratic kernel.