An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
الموضوعات :
Ramesh G
1
(Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.)
Prasanna G B
2
(Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.)
Santosh V Bhat
3
(Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.)
Chandrashekar Naik
4
(Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.)
Champa H N
5
(Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.)
الکلمات المفتاحية: Computer Vision Dimensionality Reduction, Handwritten Digit Recognition, Kannada-MNIST Dataset, PCA, SVM.,
ملخص المقالة :
Handwritten digit recognition is one of the classical issues in the field of image grouping, a subfield of computer vision. The event of the handwritten digit is generous. With a wide opportunity, the issue of handwritten digit recognition by using computer vision and machine learning techniques has been a well-considered upon field. The field has gone through an exceptional turn of events, since the development of machine learning techniques. Utilizing the strategy for Support Vector Machine (SVM) and Principal Component Analysis (PCA), a robust and swift method to solve the problem of handwritten digit recognition, for the Kannada language is introduced. In this work, the Kannada-MNIST dataset is used for digit recognition to evaluate the performance of SVM and PCA. Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to the lack of a standard MNIST dataset for Kannada numerals, Kannada Handwritten digit recognition was left behind. With the introduction of the MNIST dataset for Kannada digits, we budge towards solving the problem statement and show how applying PCA for dimensionality reduction before using the SVM classifier increases the accuracy on the RBF kernel. 60,000 images are used for training and 10,000 images for testing the model and an accuracy of 99.02% on validation data and 95.44% on test data is achieved. Performance measures like Precision, Recall, and F1-score have been evaluated on the method used.
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