Linear discriminant analysis in Ottoman alphabet character recognition

Zeyneb Kurt*, H. Irem Turkmen, M. Elif Karsligil

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the European Computing Conference
Number of pages7
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventEuropean Computing Conference - Athens, Greece
Duration: 25 Sept 200727 Sept 2007

Publication series

NameLecture Notes in Electrical Engineering
Volume28 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceEuropean Computing Conference


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