Preprint Article Version 2 This version is not peer-reviewed

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

Version 1 : Received: 1 March 2019 / Approved: 4 March 2019 / Online: 4 March 2019 (10:32:36 CET)
Version 2 : Received: 19 September 2019 / Approved: 20 September 2019 / Online: 20 September 2019 (10:12:26 CEST)

How to cite: Siddique, F.; Sakib, S.; Siddique, M.A.B. Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers. Preprints 2019, 2019030039 (doi: 10.20944/preprints201903.0039.v2). Siddique, F.; Sakib, S.; Siddique, M.A.B. Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers. Preprints 2019, 2019030039 (doi: 10.20944/preprints201903.0039.v2).

Abstract

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

Subject Areas

Handwritten digit recognition; Convolutional Neural Network (CNN); Deep learning; MNIST dataset; Epochs; Hidden Layers; Stochastic Gradient Descent; Backpropagation

Comments (1)

Comment 1
Received: 20 September 2019
Commenter: Shadman Sakib
Commenter's Conflict of Interests: Author
Comment: The name of the paper has been changed according to the reviewers' comments of ICAEE 2019 (http://www.icaeeiub.net/). Also, whole paper is modified and grammatically corrected.
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