Jul 2026· Journal of Sciences and Engineering· 0 citations· 9 references
TL;DR
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.
Abstract
This study presents an easy-to-use system that can recognize and solve handwritten polynomial equations using a Convolutional Neural Network (CNN). To achieve this, we began by collecting digits and math symbols from various publicly available sources. We began with a dataset of around 30,000 samples, then converted the images to grayscale, inverted them, applied binary thresholding, and removed noise to clean them up. Individual symbols are then separated using OpenCV, and a custom-trained CNN model classifies each symbol. We used 16 classes to classify our data. To ensure the model’s ability to detect symbols increases and produces efficient results, we expanded our data set through data augmentation to over 100, 000 images. CNN, built using Keras, achieved an impressive 98.99% classification accuracy, reliably identifying each character during training. Once the symbols are recognized, they are combined to form a complete equation. For normal equations, the system uses the eval () function to evaluate expression, providing quick, dynamic solutions . For polynomial equations of up to the third degree, SymPy is utilized for symbolic computation, providing accurate and detailed solutions. The solution is presented through a PyQt5-based user interface, that allows users to upload their handwritten equations and view the solved results. The proposed method supports polynomial equations with variables and basic mathematical operators, providing an accurate and user-friendly educational tool. The system supports basic mathematical symbols (+, –, ×, =, x, y). It can handle polynomial equations up to the third degree, which will be beneficial for students learning algebra or for quick problem solving.
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
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.
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
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.
Oluwadamilare (Asabia) Joseph Omoniyi, Omotosho Olawale Jacob, Ajaegbu Chigozirim et al.· International journal of res...· 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
