An Efficient Method for Handwritten Kannada Digit Recognition based on PCA and SVM Classifier
الموضوعات :Ramesh G 1 , Prasanna G B 2 , Santosh V Bhat 3 , Chandrashekar Naik 4 , Champa H N 5
1 - Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.
2 - Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.
3 - Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.
4 - Department of Computer Science & Engineering, University Visvesvaraya College of Engineering, Bengaluru, India.
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.
[1] R. R. Kunte and R. Samuel, “170wavelet features based on-line recognition of handwritten,” Journal of the Visualization Society of Japan, vol. 20, no. 1, pp. 417–420, 2000.
[2] G. Rajput, H. Rajeswari, and C. Sidramappa, “Printed and handwritten kannada numeral recognition using crack codes and fourier descriptors plate,” International Journal of Computer Application (IJCA) on Recent Trends in Image Processing and Pattern Recognition (RTIPPR)}, pp. 53-58, 2010.
[3] C. Chiang, R.-H. Wang, and B.-R. Chen, “Recognizing arbitrarily connected and superimposed handwritten numerals in intangible writing interfaces,” Pattern Recognition, {Elsevier} vol. 61, pp. 15--28, 2017.
[4] M. H. Ramappa and S. Krishnamurthy, “A comparative study of different feature extraction and classification methods for recognition of hand- written kannada numerals,” International Journal of Database Theory & Application, vol. 6, no. 4, pp. 71–90, 2013.
[5] B.V.Dhandra, G. Mukarambi, and M. Hangarge, “Zone based features for handwritten and printed mixed kannada digits recognition,” IJCA Proceedings on International Conference on VLSI, Communications and Instrumentation (ICVCI), no. 7, pp. 5–8, 2011.
[6] S. Karthik and K. Murthy, “Handwritten kannada numerals recognition using histogram of oriented gradient descriptors and support vector machines,” Advances in Intelligent Systems and Computing, vol.2, pp. 51–57, 2015.
[7] S. V. Rajashekararadhya and P. Vanaja Ranjan, “Neural network based handwritten numeral recognition of kannada and telugu scripts,” in TENCON 2008 - 2008 IEEE Region 10 Conference, pp. 1–5, 2008.
[8] G. Rajput, Horakeri, Rajeswari, and C. Sidramappa, “Printed and handwritten mixed kannada numerals recognition using svm,” International Journal on Computer Science and Engineering, vol. 2, pp. 1622- 1626, 2010.
[9] V. Hallur and R. Hegadi, “Offline kannada handwritten numeral recognition: Holistic approach,” Proceeding of Second International Conference on Emerging Research in Computing, Information, Communication and Applications, vol. 3, pp. 632-637, 2014.
[10] U. Pal, N. Sharma, T. Wakabayashi, and F. Kimura, “Handwritten numeral recognition of six popular indian scripts,” in Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), vol. 2, pp. 749–753, 2007.
[11] F. Bovolo, L. Bruzzone, and L. Carlin, “A novel technique for subpixel image classification based on support vector machine,” IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2983–2999, 2010.
[12] M. Maloo and K. Kale, “Support vector machine based gujarati numeral recognition,” International Journal of Computer Science Engineering (IJCSE), ISSN 0975-3397, vol. 3, pp. 2595–2600, 07 2011.
[13] H. Sajedi, “Handwriting recognition of digits, signs, and numerical strings in persian,” Computers Electrical Engineering, vol. 49, pp. 52– 65, 01 2016. [14] W. Lu, “Handwritten digits’ recognition using pca of histogram of oriented gradient,” in 2017 IEEE Pacific Rim Conference on Communi- cations, Computers and Signal Processing (PACRIM), pp. 1–5, 2017.
[15] E. S. GATI, B. D. NIMO, and E. K. ASIAMAH, “Kannada-mnist classification using skip cnn,” in 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Pro- cessing, pp. 245–248, 2019.
[16] G. Jha and H. Cecotti, “Data augmentation for handwritten digit recognition using generative adversarial networks,” Multimedia Tools and Applications, pp. 1–14, 2020.
[17] S. Aly and S. Almotairi, “Deep convolutional self-organizing map network for robust handwritten digit recognition,” IEEE Access, vol. 8, pp. 107035–107045, 2020.
[18] V. U. Prabhu, “Kannada-mnist: A new handwritten digit’s dataset for the kannada language,” arXiv preprint arXiv:1908.01242, 2019.
[19] H. Cocotte, “Active graph based semi-supervised learning using image matching: application to handwritten digit recognition”, Pattern Recognition Letters, vol. 73, pp. 76--82, 2016.
[20] Hallur, Vishweshwrayya C., and R. S. Hegadi. "Handwritten Kannada numerals recognition using deep learning convolution neural network (DCNN) classifier." CSI Transactions on ICT, vol. 8, pp. 295-309, 2020.
[21] Aly, Saleh, and Ahmed Mohamed. "Unknown-length handwritten numeral string recognition using cascade of pca-svmnet classifiers." IEEE Access vol. 7, pp. 52024-52034. 2019.
[22] UÇAR, Emine, and Murat UÇAR. "Applying Capsule Network on Kannada-MNIST Handwritten Digit Dataset." Natural and Engineering Sciences (2019).
[23] Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing." (2002).
