•  
  •  
 

Corresponding Author

Raid Ismael Mohammed

Document Type

Article

Keywords

MNIST, Convolutional neural networks, Lightweight models, Handwritten digit recognition, Deep learning

Abstract

Handwritten digit recognition is a classic computer vision task, often used as a benchmark for image classification models. This paper presents a lightweight convolutional neural network (CNN) baseline for offline handwritten digit recognition using the MNIST dataset. The proposed CNN has a simple architecture (two convolutional layers with max-pooling, followed by a flattening and two fully connected layers) and is designed to be computationally efficient while achieving high accuracy. We train the model with standard settings (Adam optimizer, batch size 128, ∼10 epochs) and obtain ∼98–99% recognition accuracy on MNIST. We provide a detailed evaluation including training curves and a confusion matrix to analyze performance per class. Despite its simplicity (only ∼37k parameters), the model's accuracy approaches that of more complex networks, confirming that even a small CNN can serve as a strong baseline for MNIST. We discuss the model's performance relative to other approaches (e.g., SVM, KNN, deeper CNNs), its limitations on this virtually solved task, and directions for future work such as applying the design to more complex datasets or optimizing it for embedded deployment.loyment.

Download

Share

COinS