Performance Comparison of Convolutional Neural Network and Long Short-Term Memory for the Classification of Handwritten Digits

Oluwatobi Joel Toyobo, Stephen Olatunde Olabiyisi, Wasiu Oladimeji Ismaila and Adebayo Olalere Oyedele
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Oluwatobi Joel Toyobo: Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo state Nigeria
Stephen Olatunde Olabiyisi: Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo state Nigeria
Wasiu Oladimeji Ismaila: Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo state Nigeria
Adebayo Olalere Oyedele: Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo state Nigeria

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 6, 565-572

Abstract: Handwritten digit recognition, a task in computer vision, is critical for applications such as postal automation, banking, and digitization of forms. Traditional approaches have leveraged statistical models, but the rise of deep learning, particularly Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM), has revolutionized the field. However, a comprehensive performance comparison of CNN and LSTM architectures in the context of handwritten digit classification remains underexplored. This study aimed to address this gap by evaluating and comparing CNN and LSTM models for the classification of handwritten digits. Two machine learning models – Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) were trained with the preprocessed handwritten digit dataset. The CNN model was designed with multiple convolutional and pooling layers, along with dropout for regularization. The LSTM model was designed with LSTM layers to capture sequential patterns in the data, followed by a dense layer for classification. The models were implemented in python, evaluated and compared based on accuracy, precision, recall and F1-score. The evaluation and comparison results indicate that CNN achieved 99.31% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score, while LSTM achieved 98.90% accuracy, 99.0% precision, 99.0% recall, and a 99.0% F1-score. The results demonstrated that CNN outperformed LSTM in terms of accuracy and misclassification errors, making it the optimal choice for image-based handwritten digit recognition. This finding underscores the efficiency of CNN in addressing challenges related to digit recognition, contributing to the advancement of automated digit classification systems and improving the accuracy of image-based classification tasks.

Date: 2025
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