Handwritten digit classification using neural networks with Tensorflow and Keras
Department of Computer Science and Engineering, School of Science, Guru Ghasidas Vishwavidyalaya, Bilaspur, C.G, India.
Research Article
Open Access Research Journal of Science and Technology, 2026, 16(02), 061-068.
Article DOI: 10.53022/oarjst.2026.16.2.0022
Publication history:
Received on 02 February 2026; revised on 09 March 2026; accepted on 12 March 2026
Abstract:
Handwritten digit recognition is an important problem in the field of machine learning and computer vision, which is used in many practical applications such as document digitization, banking systems, postal automation, and academic assessment. Automatic identification of handwritten digits becomes a challenging task due to their variety in writing style, shape and thickness. The paper designed, implemented and evaluated a deep learning model based on the Convolutional Neural Network (CNN) using TensorFlow and Keras to accurately classify handwritten scores.The study used the MNIST dataset, which includes 60,000 training samples and 10,000 test samples in the form of grayscale images of 28 × 28 size. Under data pre-processing, techniques like normalization, re-reasoning and label encoding were adopted. The proposed CNN model incorporated convolution layers, max-pooling layers, fully-connected layers, and Softmax output layers, which enabled effective acquisition of spatial characteristics of handwritten digits.It was evident from the experimental results that the model achieved high classification accuracy, the difference between training and validation accuracy was minimal and the reliability of the classification was confirmed by confusion matrix analysis. The findings of the study indicate that the CNN-based deep learning model provides an accurate, reliable and practical solution for identifying handwritten marks.
Keywords:
Handwritten digit recognition; Convolutional neural networks (CNNs); MNIST dataset; Deep learning; TensorFlow; Keras
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