Ibukunoluwa Adetutu OLAJIDE, Emmanuel Alechenu


The topic of transcribed digit acknowledgment has been an open one in the field of pattern recognition for a long time. Several studies have shown that Neural Networks perform exceptionally well in data organization. The main goal of this study is to provide effective and reliable approaches for recognizing transcribed numerical data by examining various existing arrangement model. The Convolutional Neural Network (CNN) from Machine Learning can be used to recognize handwritten digits. The core structure of this research development is based on the MNIST (Modified National Institute of Standards and Technologies) database and CNN compilation. So, in order to run the model, libraries like NumPy, Pandas, TensorFlow, and Keras were employed. These are the fundamental pillars that the design is built upon. MNIST has roughly 60,000 photos of handwritten numbers ranging from 0 to 9. As a result, it is a classification model of class 10. The dataset is split into two parts: training and testing with Image representation as a 28*28 matrix with grayscale pixels in each cell. The result display of Convolutional Neural Networks (CNN) is the subject of this paper, which shows that the CNN classifier outperformed the Neural Network in terms of computing efficiency without sacrificing execution time. Notably, the combination of pre-processing and CNN reached the highest recognition rate of 99.16% in the experiment.

Full Text:



Das, N., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M., & Basu, D. K. A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application. Applied Soft Computing, 12(5), 1592-1606, 2012.

Plamondon, R., & Srihari, S. N. Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84.

Shamim, S.; Miah, M.B.A.; Angona Sarker, M.R.; Al Jobair, A.: Handwritten digit recognition using machine learning algorithms. Global J. Comput. Sci. Technol. 18(1), 1–8 (2018).

Seewald, A. K. (2011). On the brittleness of handwritten digit recognition models. ISRN Machine Vision, 2012.

Vijayalaxmi R Rudraswamimath, and Bhavanishankar K , Handwritten Digit Recognition using CNN , International Journal of Innovative Science and Research Technology, Volume 4, Issue 6, June – 2019

Saleh Albahli, Marriam Nawaz , Ali Javed, and Aun Irtaza, An improved faster?RCNN model for handwritten character recognition, Arabian Journal for Science and Engineering march 2021.

Ritik Dixit ,Rishika Kushwah and Samay Pashine , Handwritten Digit Recognition using Machine and Deep Learning Algorithms, International Journal of Computer Applications (0975 – 8887) Volume 176 – No. 42, July 2020.

Dayan, P. "Unsupervised Learning," The MIT Encyclopedia of the Cognitive Sciences, Massachusetts, 1999.

Kotsiantis,S.B, "Supervised Machine Learning: A Review of Classification Techniques," Department of Computer Science and Technology, University of Peloponnese, Peloponnese, Greece, 2007.


  • There are currently no refbacks.

Copyright (c) 2023 Journal of Electrical Engineering, Electronics, Control and Computer Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.