Connected and degraded text recognition using planar hidden Markov models

An algorithm for connected text recognition using enhanced planar hidden Markov models (PHMMs) is presented. The algorithm automatically segments text into characters (even if they are highly blurred and touching) as an integral part of the recognition process, thus jointly optimizing segmentation and recognition. Performance is enhanced by the use of state length models, transition probabilities among characters (bigrams), and grammars. Experiments are presented using: (1) a simulated database of over 24000 highly degraded images of city names and (2) a database of 6000 images rejected by a high-performance commercial OCR (optical character recognition) machine with 99.5% accuracy. Measured performance on the first database is 99.65% for the most degraded images when a grammar is used, and 98.76% in the second database. Traditional OCR algorithms would fail drastically on these images.<>