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.
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.
R. Rajesh, P. Sravani· International Journal for Re...· 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 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
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
In this paper, a comparative study between handcrafted and automated feature extraction method has been provided. The handcrafted method has been based over local binary pattern (LBP) as feature extraction technique. The histogram equalization (HE), multi-scale retinex (MSR), and a difference of Gaussian (DOG) have been used as a preprocessing technique to improve the image quality. The results of the handcrafted approach have been shown that the performance with HE is the best. In the automated part, ALEXNET has been used as convolutional neural network (CNN) architecture. The standard gradient descent with momentum (SGDM) has been used as the optimizer, because the results were better when it has been used. The results of the automated part have been shown how the layers activation functions works. In the automated part, the training and test accuracy have been evaluated and compared between different databases. The accuracy has achieved up to 100% in Face94 and Face grimace databases as the best accuracy in the CNN approach.
Lana Abdullah AL-Afeef, H. Al-Otum· International Journal of Fut...· 0 citations