Handwritten Mathematical Symbols Classification Using WEKA

Machine learning tools have been extensively used for the prediction and classification of mathematical symbols, formulas, and expressions. Although the recognition and classification in handwritten text and scripts have reached a point of commensurate maturity, yet the recognition work related to mathematical symbols and expressions has remained a stimulating and challenging task throughout. So, in this work, we have used Weka, a machine learning tool, for the classification of handwritten mathematical symbols. The current literature witnesses a limited amount of research works for classification for handwritten mathematical text using this tool. We have endeavored to explore the potential classification rate of handwritten symbols while analyzing the performance by comparing the results obtained by several clustering, classification, regression, and other machine learning algorithms. The comparative analysis of 15 such algorithms has been performed, and the dataset used for the experiment incorporates selective handwritten math symbols. The experimental results output accuracy of 72.9215% using the Decision Table algorithm.