Performance Analysis of Digit Recognizer Using Various Machine Learning Algorithms

Author:

Chittem Lakshmi Alekya,Logofatu Doina,Mim Sheikh Sharfuddin

Publisher

Springer Nature Switzerland

Reference12 articles.

1. Chychkarova, Y., Serhiienkob, A., Syrmamiikha, I., Karginc, A.: Handwritten Digits Recognition Using SVM, KNN, RF and Deep learning Algorithms. https://ceur-ws.org/Vol-2864/paper44.pdf. Accessed 23 Feb 2023

2. Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recogn. 45(4), 1318–1325 (2012)

3. Jain, M., Kaur, G., Quamar, M.P., Gupta, H.: Handwritten digit recognition using CNN. In: 2021 International Conference on Innovative Practices in Technology and Management (ICIPTM), Noida, India, pp. 211–215 (2021)

4. Al Mansoori, S.: Intelligent handwritten digit recognition using artificial neural network. Int. J. Eng. Res. Appl. 5(5(Part -3)), 46–51 (2015). https://www.ijera.comISSN. ISSN : 2248–9622

5. Ahamed, H., Alam, S.M.I., Islam, M.M.: SVM based real time hand-written digit recognition system. In: Conference: International Conference on Engineering Research and Education School of Applied sciences & Technology, Sylhet (2019). ] https://www.researchgate.net/publication/330684489_SVM_Based_Real_Time_Hand-Written_Digit_Recognition_System

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1. Use of Machine Learning Algorithms to Analyze the Digit Recognizer Problem in an Effective Manner;Artificial Neural Networks and Machine Learning – ICANN 2023;2023

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