Robust OCR of degraded documents

This paper is concerned with techniques for performing robust OCR of degraded documents, such us faxed text, using a hidden Markov model (HMM) based OCR system. We present two strategies for dealing with degraded documents. The first strategy is to train the system on degraded documents that have been subjected to the same, or similar, degradation process as the documents to be recognized. The second, more sophisticated, strategy is to use adaptation to adjust the parameters of the trained model in order to improve recognition accuracy on a specific document. This adjustment of model parameters is typically posed as a constrained optimization problem wherein a certain prespecified objective function is to be optimized. We present a comparative study of two objective functions. The likelihood function and the posterior probability. A variation of the basic posterior probability method is also discussed. Using adaptation with a model trained on fax-degraded data we have reduced, by a factor of three, the character error rate on fax-degraded text images generated from the University of Washington English Image Database I