Recognition of Handwritten Digits using Non-linear Kernel Discriminant Analysis


I’ve just submitted a new article to CodeProject, entitled "Handwriting Recognition using Kernel Discriminant Analysis". The article is a demonstration of handwritten digit recognition using Non-linear Discriminant Analysis with Kernels and using the Optical Recognition of Handwritten Digits data set from the University of California’s Machine Learning Repository.

Recognition of handwritten digits using Non-linear Kernel Discriminant Analysis

Recognition of Handwritten digits using Kernel Discriminant Analysis


The Code Project is a free repository of source code, tutorials and development resources. It is also home of a large and ever-growing community of software developers, architects and other technology professionals. It has fairly active forums, and is a reasonably good resource for resolving difficult software development issues.


  1. I can’t find the words to express how I feel about handwriting. But your little paragraph really said what I can’t. I am a HUGE fanatic of Signature forgery expert , preferably from ancestors and it’s always a treat when one is found that is handwritten. Those I cherish the most. They are priceless.

  2. Hi Cesar,
    Your tutorial is awesome! I just started my research in kernel SVM… I referred some papers for SVM classification technique but I can’t understand the basic properly. In your tutorial you have used opdigit training sets, can you please help me how attributes can be calculated from opdigit training samples and how we are mapping it to the feature space for classification using hyperplane and recognition technique with an example. Really need your help.
    Thanking you.

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