Structural information implant in a context based segmentation-free HMM handwritten word recognition system for Latin and Bangla script

In this paper, an improvement of a 2D stochastic model based handwritten entity recognition system is described. To model the handwriting considered as being a two dimensional signal, a context based, segmentation-free hidden Markov model (HMM) recognition system was used. The baseline approach combines a Markov random field (MRF) and a HMM so-called non-symmetric half plane hidden Markov model (NSHP-HMM). To improve the results performed by this baseline system operating just on low-level pixel information an extension of the NSHP-HMM is proposed. The mechanism allows to extend the observations of the NSHP-HMM by implanting structural information in the system. At present, the accuracy of the system on the SRTP1 French postal check database is 87.52% while for the handwritten Bangla city names is 86.80%. The gain using this structural information for the SRTP dataset is 1.57%.