Offline Handwritten Gurmukhi Character Recognition: Analytical Study of Different Transformations

Research Article


In this paper, we have presented an offline handwritten Gurmukhi character recognition system using various transformations techniques, namely, discrete wavelet transformations (DWT2), discrete cosine transformations (DCT2), fast Fourier transformations and fan beam transformations. DWT2 has also been considered with three different types, namely, Haar wavelet, Daubechies (db) 1 wavelet and Daubechies (db) 2 wavelet transformations. In this work, we have used support vector machine (SVM) classifier for classification, as well as for linear kernel and polynomial kernel. For the purpose of training and testing data set, we have collected around 10,500 samples of isolated offline handwritten Gurmukhi characters. After due experiments with the help of 5-fold cross validation technique using DCT2 coefficients as features, 95.8 % recognition accuracy has been achieved with SVM for linear kernel.


Offline Handwritten character recognition Feature extraction Classification SVM 


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Copyright information

© The National Academy of Sciences, India 2016

Authors and Affiliations

  1. 1.Department of Computer SciencePanjab University Rural CentreKauni, MuktsarIndia
  2. 2.Department of Computer Science and ApplicationsPanjab University Regional CentreMuktsarIndia
  3. 3.Department of Computer Science and EngineeringThapar UniversityPatialaIndia

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