Hand Written Digit Recognition Using Machine Learning


  • Atmakuri Venkata Siva Rama Praneeth
  • Lokesh Reddy Pappu
  • Gannarapu Pavan Kumar
  • Aparna Sachidanand Bhatta
  • Boddu Rashmi Chowdary
  • Gande Sai Shishwan


Machine Learning, Digit recognition


In current instances, with the growth of Artificial Neural Network (ANN), deep getting to know has delivered a dramatic twist in the area of system gaining knowledge of by means of making it more artificially intelligent. Deep learning is remarkably used in big degrees of fields due to its numerous range of packages which include surveillance, health, medication, sports activities, robotics, drones, and so forth. In deep studying, Convolutional Neural Network (CNN) is on the center of remarkable advances that combines Artificial Neural Network (ANN) and up to date deep getting to know techniques. It has been used extensively in sample recognition, sentence category, speech recognition, face recognition, textual content categorization, report evaluation, scene, and handwritten digit reputation. The intention of our project is to examine the variant of accuracies of CNN to categorize handwritten digits the use of numerous numbers of hidden layers and epochs and to make the comparison among the accuracies. In this undertaking, we've got chosen to attention on recognizing handwritten digits to be had within the MNIST database.


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How to Cite

A. V. S. R. Praneeth, L. R. Pappu, G. P. Kumar, A. S. Bhatta, B. R. Chowdary, and G. S. Shishwan, “Hand Written Digit Recognition Using Machine Learning”, IJRESM, vol. 5, no. 1, pp. 59–62, Jan. 2022.




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