Characteristic mining of Mathematical Formulas from Document - A Comparative Study on Sequence Matcher and Levenshtein Distance procedure - Appa Rao, G; Srinivas, G; Venkata Rao, K; Prasad Reddy, PVGD (International Journal of Computer Sciences and Engineering, 2025)
The key predicament in the present circumstances is how to categorize the mathematically related keywords from a given text file and store them in one math text file. As the math text file contains only the keywords which are related to mathematics. The math dataset is a collection of huge amount of tested documents and stored in math text file. The dataset is trained with giant amount of text files and the size of dataset increases, training with various text samples. Finally the dataset contains only math-related keywords. The proposed approaches evaluated on the text containing individual formulas and repeated formulas. The two approaches proposed are one is Sequence matcher and another one is Levenshtein Distance, both are used for checking string similarity. The performance of the repossession is premeditated based on dataset of repetitive formulas and formulas appearing once and the time taken for reclamation is also measured.