Handwritten Polynomial Equation Recognition Using CNN and Symbolic Solution with SYMPY

Handwritten Digit Recognition Using Deep Learning

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.

Handwritten Digit Recognition Based on Convolutional Neural Network and Its Application in Overlapping Image Processing

Integrating Fuzzy Logic and Fractional-Order Operators in Convolutional Neural Networks for Handwritten Digits and Characters Recognition

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.

Handwritten Character Recognition Using Deep Learning

Handwritten Character Recognition (HCR) is a significant area of research in the field of pattern recognition and computer vision, aiming to automatically identify and convert handwritten text into machine readable digital formats. Traditional recognition methods often struggle with variations in writing styles, character shapes, and image quality. To address these challenges, this research proposes a deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks (CNNs) for automatic feature extraction and classification. The proposed model is trained on a large dataset of handwritten characters and incorporates preprocessing techniques such as image normalization, noise removal, and segmentation to improve recognition accuracy. Deep learning enables the system to learn complex patterns and hierarchical features directly from raw image data, eliminating the need for manual feature engineering. Experimental results demonstrate that the proposed approach achieves high accuracy, robustness, and generalization across different handwriting styles. The system effectively recognizes handwritten alphabets, numerals, and special characters, making it suitable for applications such as document digitization, postal mail sorting, bank cheque processing, educational assessment systems, and historical manuscript preservation. The study highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional machine learning methods. Future enhancements may include multilingual character recognition, real-time processing capabilities, and integration with advanced architectures such as transformers and recurrent neural networks to further improve accuracy and efficiency. Index Terms - Handwritten Character Recognition, Deep Learning, Convolutional Neural Network, Image Processing, Pattern Recognition, Optical Character Recognition, Machine Learning.

Comparative Analysis of Custom and Pre-Trained Convolutional Neural Networks (CNNs) for Object Recognition on the Cifar-10 Dataset

Convolutional Neural Networks (CNNs) have significantly changed image classification over the years by allowing computers to learn features directly from raw pixel data. However, deciding between building a customised model and using a pre-trained one can be a difficult task, especially when working with small datasets. In this study, we compare a custom CNN with three pre-trained models—VGG16, ResNet50, and MobileNetV2—on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories. We measured model performance using accuracy, precision, recall, F1-score, and training time. The results show that pre-trained models performed much better than the customised model. ResNet50 had the highest accuracy at 92.4%. However, MobileNetV2 gave the best mix of speed (1,800 seconds to train) and accuracy (90.2%). The custom CNN reached 82.3% accuracy, used less memory, and did not need image resizing. These results offer clear benchmarks for choosing models in the face of limited resources. They also demonstrate that transfer learning can achieve strong performance, while showing that custom CNNs remain useful for learning and simple tasks.

Performance Comparison of Support Vector Machine and Decision Trees for the Classification of Handwritten Digits

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