AN IMPROVED ALGORITHM OF OPTICAL FORMULA EXTRACTION WITH FUZZY CLASSIFICATION
Ming-Hu Ha, Xuedong Tian · International Journal of Pattern Recognition and Artificial Intelligence · 2008
Formula extraction is the first stage of optical formula recognition which converts printed scientific documents into their corresponding electronic format. So far, little research has been done in this area. In this paper, an improved method using fuzzy classification and irregularity rate feature is proposed to separate formulas from texts in the printed documents. Firstly, according to a statistical threshold of distance, connected components are extracted and merged to form the areas of characters and lines. Secondly, the isolated formulas are extracted based on the line features. Finally, the formula symbols in the rest lines are labeled using irregularity degree, and the embedded formulas are located by extending kernel symbols using the propagation of context. In these steps, fuzzy classification algorithm and irregularity degree feature are introduced to solve the problems existing in traditional methods and improve the extracting accuracy. The experimental results show that the method is of great significance in both theory and practice.