[EN] This paper describes a formal model for the recognition of on-line handwritten mathematical expressions
using 2D stochastic context-free grammars and hidden Markov models. Hidden Markov models are used
to recognize mathematical symbols, and a stochastic context-free grammar is used to model the relation
between these symbols. This formal model makes possible to use classic algorithms for parsing and stochastic
estimation. In this way, first, the model is able to capture many of variability phenomena that
appear in on-line handwritten mathematical expressions during the training process. And second, the
parsing process can make decisions taking into account only stochastic information, and avoiding heuristic
decisions. The proposed model participated in a contest of mathematical expression recognition and it
obtained the best results at different levels.
2012 Elsevier B.V. All rights reserved.Work supported by the EC (FEDER/ FSE) and the Spanish MEC/MICINN under the MIPRCV ‘‘Consolider Ingenio 2010’’ program (CSD2007-00018), the MITTRAL (TIN2009-14633-C03-01) project, the FPU Grant (AP2009-4363), and by the Generalitat Valenciana under the Grant Prometeo/2009/014.Álvaro Muñoz, F.; Sánchez Peiró, JA.; Benedí Ruiz, JM. (2014). Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Pattern Recognition Letters. 35:58-67. https://doi.org/10.1016/j.patrec.2012.09.023S58673
