Hidden markov model based optical character recognition in the presence of deterministic transformations

摘要

A method is introduced to combine and jointly optimize recognition and image normalization in optical character recognition algorithms based on pseudo two-dimensional (2D) hidden Markov models (HMMs). The method can be combined with a previous method for joint segmentation and recognition of connected text. It also provides a maximum likelihood estimate of the transformation parameters (scaling factor, slant angle, etc.), that can be used by higher level modules in an intelligent document recognition system as an aid in the recognition process. The computational cost of this technique is modest. Experimental results on a data base of distorted printed characters are presented.