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has been cited by the following article:

• TITLE: Mathematical Expression Extraction in Text Fields of Documents Based on HMM

  AUTHORS: Xuedong Tian, Ruihan Bai, Fang Yang, Jinyuan Bai, Xinfu Li

  KEYWORDS: Mathematical Expression Extraction, Hidden Markov Model, Text Fields, Documents, Symbol Combination Features

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.14, December 12, 2017

  ABSTRACT: Aiming at the problem that the mathematical expressions in unstructured text fields of documents are hard to be extracted automatically, rapidly and effectively, a method based on Hidden Markov Model (HMM) is proposed. Firstly, this method trained the HMM model through employing the symbol combination features of mathematical expressions. Then, some preprocessing works such as removing labels and filtering words were carried out. Finally, the preprocessed text was converted into an observation sequence as the input of the HMM model to determine which is the mathematical expression and extracts it. The experimental results show that the proposed method can effectively extract the mathematical expressions from the text fields of documents, and also has the relatively high accuracy rate and recall rate.