Title
An integrated grammar-based approach for mathematical expression recognition
Abstract
Automatic recognition of mathematical expressions is a challenging pattern recognition problem since there are many ambiguities at different levels. On the one hand, the recognition of the symbols of the mathematical expression. On the other hand, the detection of the two-dimensional structure that relates the symbols and represents the math expression. These problems are closely related since symbol recognition is influenced by the structure of the expression, while the structure strongly depends on the symbols that are recognized. For these reasons, we present an integrated approach that combines several stochastic sources of information and is able to globally determine the most likely expression. This way, symbol segmentation, symbol recognition and structural analysis are simultaneously optimized. In this paper we define the statistical framework of a model based on two-dimensional grammars and its associated parsing algorithm. Since the search space is too large, restrictions are introduced for making the search feasible. We have developed a system that implements this approach and we report results on the large public dataset of the CROHME international competition. This approach significantly outperforms other proposals and was awarded best system using only the training dataset of the competition. HighlightsAn integrated mathematical expression recognition system is proposed.This system integrates several knowledge sources.The learning of the system is described.Experiments with public databases are reported.
Year
DOI
Venue
2016
10.1016/j.patcog.2015.09.013
Pattern Recognition
Keywords
Field
DocType
Mathematical expression recognition,Probabilistic parsing,Handwriting recognition
Rule-based machine translation,Expression (mathematics),Recognition system,Pattern recognition,Computer science,Segmentation,Symbol,Handwriting recognition,Grammar,Artificial intelligence,Parsing,Machine learning
Journal
Volume
Issue
ISSN
51
C
0031-3203
Citations 
PageRank 
References 
7
0.46
34
Authors
5