Combining Multiple Feature Extraction Technique and Classifiers for Increasing Accuracy for Devanagari OCR
Anilkumar N Holambe1, Ravindra C Thool2
1Anilkumar N Holambe, Shri Guru Gobind Singhji Institute Of Engineering & Technology, Nanded, India.
2Dr.Ravindra C Thool. Shri Guru Gobind Singhji Institute Of Engineering & Technology, Nanded, India.
Manuscript received on August 05, 2013. | Revised Manuscript received on August 28, 2013. | Manuscript published on September 05, 2013. | PP: 38-41 | Volume-3, Issue-4, September 2013. | Retrieval Number: D1749093413/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we combining statistical, structural, Global transformation and moments features to form hybrid feature vector .We are combining Classifiers for achieving high accuracy for Devanagari Script. To abolish the hitch of misclassification and increase the classifier accuracy, we are combining SVM and KNN together. The dataset used for experiment are created by us.
Keywords: Devanagari, SVMKNN, Features, Zernike moment.