Ahmet Kal'a

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.

A Backpropagation Neural Network Approach For Ottoman Character Recognition

Abstract The Ottoman Empire established in 1299 and continued 6 centuries covering an area of about 5.6 million squared km. The Empire left a large collection of valuable archives interesting to historians from all over the world. Investigation and understanding these documents will shed light on the history of the world In order to achieve access of the considered information by worldwide scientists, it is essential to translate Ottoman characters into Latin alphabet. Thus, we aimed to recognize the Ottoman characters using Artificial Neural Network (ANN) and compazed it with Support Vector Machine (SVM) approaches. We used printed type of Ottoman scripts in image acquisition. Pre-processing such as normalization and edge detection were implemented. Multilayer perceptions of ANN were trained using the backpropagation learning algorithm. As a result of our research, we are able to classify the Ottoman chazacters with 85.5% classification accuracy using the proposed recognition system.