Paper Info
Title
A global learning approach for an online handwritten mathematical expression recognition system
Abstract
Despite the recent advances in handwriting recognition, handwritten two-dimensional (2D) languages are still a challenge. Electrical schemas, chemical equations and mathematical expressions (MEs) are examples of such 2D languages. In this case, the recognition problem is particularly difficult due to the two dimensional layout of the language. This paper presents an online handwritten mathematical expression recognition system that handles mathematical expression recognition as a simultaneous optimization of expression segmentation, symbol recognition, and 2D structure recognition under the restriction of a mathematical expression grammar. The originality of the approach is a global strategy allowing learning mathematical symbols and spatial relations directly from complete expressions. A new contextual modeling is proposed for combining syntactic and structural information. Those models are used to find the most likely combination of segmentation/recognition hypotheses proposed by a 2D segmentation scheme. Thus, models are based on structural information concerning the symbol layout. The system is tested with a new public database of mathematical expressions which was used in the CHROME competition. We have also produced a large base of semi-synthetic expressions which are used to train and test the global learning approach. We obtain very promising results on both synthetic and real expressions databases, as well as in the recent CHROME competition.
Year
DOI
Venue
2014
10.1016/j.patrec.2012.10.024
Pattern Recognition Letters
Keywords
Field
DocType
recognition system,structure recognition,structural information,handwriting recognition,mathematical expression,recognition problem,mathematical expression recognition,global learning approach,mathematical symbol,symbol recognition,mathematical expression grammar,online handwritten mathematical expression
Expression (mathematics),Computer science,Handwriting recognition,Feature (machine learning),Natural language processing,Artificial intelligence,Syntactic pattern recognition,Syntax,Computer vision,Pattern recognition,Intelligent character recognition,Segmentation,Sketch recognition,Machine learning
Journal
Volume
ISSN
Citations 
35,
0167-8655
22
PageRank 
References 
Authors
0.86
47
3
Name
Order
Citations
PageRank
Ahmad-Montaser Awal1837.01
Harold Mouchère210714.46
Christian Viard-Gaudin344446.20