A Data-Informatics-Oriented CNN-Based Intelligent Model for Handwritten Mathematical Symbol Recognition

Handwritten mathematical symbol recognition remains a challenging problem due to variations in writing styles, stroke structures, and visual similarities among symbols, which often reduce classification accuracy. This study proposes a data-informatics-oriented convolutional neural network (CNN) model for robust recognition of handwritten mathematical symbols. The research adopts a supervised experimental design using a balanced dataset consisting of six mathematical symbol classes. A systematic preprocessing pipeline including image resizing, normalization, and structured dataset partitioning is implemented to ensure data consistency and improve feature learning. The CNN model is implemented in MATLAB and optimized using stochastic gradient descent with momentum. Model performance is evaluated using confusion matrix–based metrics, including accuracy, precision, recall, and F1-score, along with computational time analysis. Experimental results demonstrate stable performance across multiple experimental runs, achieving an average accuracy of 97.08%, precision of 97.10%, recall of 97.08%, and F1-score of 97.07%. Confusion matrix analysis indicates that most handwritten symbols are correctly classified, with only minor misclassifications occurring among visually similar operators. These results confirm the effectiveness of integrating data informatics principles with CNN-based feature learning for handwritten mathematical symbol recognition. The proposed framework provides a reliable foundation for intelligent systems supporting digital education, automated assessment, and mathematical document digitization.