Abstract
Handwritten digit recognition is one of the classical problems in the field of image classification, a subfield of computer vision. In this work, we propose an approach for recognition of handwritten digits for Kannada language. We have used Kannada-MNIST dataset for digit recognition to evaluate the performance of Support Vector Machines (SVM) and Principal Component Analysis (PCA). Efforts were made previously to recognize handwritten digits of different languages with this approach. However, due to lack of standard MNIST dataset for Kannada numerals, Kannada handwritten digit recognition was left behind and very less work has taken place in this aspect. With the introduction of 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. The system is trained using 60,000 and 10,000 images were used for testing. We were able to achieve accuracy of 95.44%. We have also evaluated our technique based on precision.
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Ramesh, G., Prasanna, G.B., Santosh, V.B., Chandrashekar, N., Champa, H. (2022). KHDR: Kannada Handwritten Digit Recognition Using PCA and SVM Classifier. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_20
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DOI: https://doi.org/10.1007/978-981-16-4807-6_20
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