Linear Discriminant Analysis in Ottoman Alphabet Character Recognition

  • Zeyneb Kurt
  • H. Irem Turkmen
  • M. Elif Karsligil
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)


This paper proposes a novel Linear Discriminant Analysis (LDA) based Ottoman Character Recognition system. Linear Discriminant Analysis reduces dimensionality of the data while retaining as much as possible of the variation present in the original dataset. In the proposed system, the training set consisted of 33 classes for each character of Ottoman language alphabet. First the training set images were normalized to reduce the variations in illumination and size. Then characteristic features were extracted by LDA. To apply LDA, the number of samples in train set must be larger than the features of each sample. To achieve this, Principal Component Analysis (PCA) were applied as an intermediate step. The described processes were also applied to the unknown test images. K-nearest neighborhood approach was used for classification.


Principal Component Analysis Linear Discriminant Analysis Character Recognition Handwritten Character Handwritten Character Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Zeyneb Kurt
    • 1
  • H. Irem Turkmen
  • M. Elif Karsligil
  1. 1.Department of Computer EngineeringYildiz Technical UniversityBesiktas/IstanbulTurkey

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