PSEUDO 2-DIMENSIONAL HIDDEN MARKOV-MODELS FOR DOCUMENT RECOGNITION

Hidden Markov models (HMM) have become the most popular technique for automatic speech recognition. Extending this technique to the two-dimensional domain is a promising approach to solving difficult problems in optical character recognition (OCR), such as recognizing poorly printed text. Hidden Markov models are robust for OCR applications due to: Their inherent tolerance to noise and distortion, Their ability to segment blurred and connected text into words and characters as an integral part of the recognition process, Their invariance to size, slant, and other transformations of the basic characters, and The ease with which contextual information and language models can be incorporated into the recognition algorithms. We give a brief overview of OCR algorithms based on two-dimensional hidden Markov models, and we present three case studies that show their remarkable strengths.