• BHOOMI GUPTA
• Vipin Garg
• Shubham Gupta

The HCR has been an interesting area of research and has numerous applications like getting information from data entry, cheque, application for loans. handwritten characters are difficult to recognize due to various writing styles and difference in size and shape of letters. In this paper handwritten characters are recognised using various supervised machine learning methods like logistic regression, random forest, k -nearest neighbour (KNN) and Convolution neural networks (CNN) which are used to get better recognition rates. We then considered the accuracy and efficiency of these strategies independently and as a whole.

handwritten recognition, machine learning, supervised learning, logistic regression, random forest, k -nearest neighbour (KNN), SVM and Convolution neural networks (CNN).

"COMPARATIVE STUDY OF VARIOUS MACHINE LEARNING ALGORITHMS IN HANDWRITTEN CHARACTER RECOGNITION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.146-152, May-2019, Available :http://www.jetir.org/papers/JETIR1905125.pdf

"COMPARATIVE STUDY OF VARIOUS MACHINE LEARNING ALGORITHMS IN HANDWRITTEN CHARACTER RECOGNITION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp146-152, May-2019, Available at : http://www.jetir.org/papers/JETIR1905125.pdf