Joint normalization and recognition of degraded document images using psuedo-2D hidden Markov models

The authors introduce a method to render optical character recognition algorithms based on pseudo two-dimensional hidden Markov models (PHMMs) independent of image transformations such as scaling, translations, slant, vertical and/or horizontal stretching, etc. Estimation of transformation parameters and image normalization are performed simultaneously with recognition. When combined with a previous method for joint segmentation and recognition of connected and degraded text, this method can be used to recognize extremely degraded documents that include characters affected by various geometric transformations. Experiments with isolated characters where scaling, slant angle, and translation are varied over ranges of 4: 1, 0° to 45°, and 0 to 40 pixels respectively, are presented. Also presented are experiments with connected text where images have been affected both by geometric and stochastic distortions of various degrees, that show the high effectiveness of this technique