Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images by using image processing techniques and a Convolutional Neural Network model trained on the MNIST dataset.
Abstract
The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to
accurately identify handwritten numerical digits from input images. The main objective of this project is to recognize digits
ranging from 0 to 9 by using image processing techniques and a Convolutional Neural Network (CNN) model trained on the
MNIST dataset. In this system, the handwritten digit image is first captured and preprocessed through steps such as grayscale
conversion, resizing, normalization, and noise reduction to improve prediction accuracy. The processed image is then passed to
the trained CNN model, which extracts important features and classifies the digit into the corresponding numerical class. The
project uses backpropagation for learning, Adam optimizer for efficient weight optimization, and Softmax activation function in
the output layer for multi-class classification. The trained model provides high accuracy and fast prediction results, making the
system suitable for real-time applications. This project demonstrates the practical implementation of deep learning in image
recognition and can be further extended for applications such as automatic form processing, postal code recognition, and bank
cheque digit identification.
A deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks for automatic feature extraction and classification and highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional machine learning methods.
Shwetha M R Shwetha M R, Kowshik S S Kowshik S S· International Scientific Jou...· 0 citations
A serial cascade of lightweight CNN and spectrum normalized GAN and spectrum normalized GAN, integrating CBAM attention mechanism is proposed, integrating CBAM attention mechanism, with good experimental results.
Handwritten digit recognition remains one of the fundamental applications of machine learning and pattern recognition due to its widespread use in banking, postal services, document processing and intelligent information systems. Although deep learning approaches have demonstrated remarkable performance, classical machine learning algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) remain attractive because of their simplicity, computational efficiency and interpretability. This study evaluates and compares the performance of Support Vector Machine and Decision Tree classifiers for handwritten digit recognition using the Modified National Institute of Standards and Technology (MNIST) dataset. The dataset comprises 70,000 grayscale images of handwritten digits (0–9), each with a resolution of 28 × 28 pixels. Image preprocessing involved normalization of pixel values and standard data preparation before model implementation. Both classifiers were implemented using Python and the Scikit-learn library under identical experimental conditions, and model performance was evaluated using accuracy, precision, recall and F1-score. Experimental results showed that the Support Vector Machine outperformed the Decision Tree across all evaluation metrics. The SVM achieved an accuracy of 94.32%, precision of 94.41%, recall of 94.32%, and an F1-score of 94.35%, whereas the Decision Tree recorded 87.36% accuracy, 87.42% precision, 87.36% recall and 87.35% F1-score. The findings indicate that SVM provides superior classification performance for handwritten digit recognition, while Decision Tree offers faster implementation and greater interpretability. The study concludes that SVM is more suitable for applications requiring high recognition accuracy, whereas Decision Tree remains appropriate for applications where computational simplicity and model transparency are prioritized
Bilikisu Temilade Azeez, Stephen Olatunde, Olabiyisi, Modupe Oluwaseun Alade et al.· International Journal of Lat...· 0 citations
This study presents an easy-to-use system that can recognize and solve handwritten polynomial equations using a Convolutional Neural Network, and supports basic mathematical symbols, providing an accurate and user-friendly educational tool.
Anupa Gaire, Rohisha Shrestha, Rosha Prajapati et al.· Journal of Sciences and Engi...· 0 citations
This study focuses on enhancing handwritten Devanagari character recognition using deep learning models, specifically fine-tuned Convolutional Neural Networks (CNNs), combined with hybrid mathematical methods for image enhancement, proposing a fuzzy-enabled Power-Law transformation for image enhancement.
Akshara Sreenivasan, Vinodkumar Vinodkumar Arumugam, Sriramakrishnan Pathmanaban et al.· Chaos and Fractals· 0 citations
A hybrid deep learning framework for Arabic handwritten digit recognition by optimizing Convolutional Neural Network hyperparameters using the Crow Search Algorithm, confirming that CSA effectively improves CNN performance while eliminating the need for manual hyperparameter tuning, making the framework suitable for other image classification tasks.
Abtisam Abdulelah Salim Azeez· Kufa journal of Engineering· 0 citations