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-dime nsional domain is a promising approach to solving difficult problems i n optical character recognition (OCR), such as recognizing poorly prin ted text. Hidden Markov models are robust for OCR applications due to: Their inherent tolerance to noise and distortion, Their ability to se gment blurred and connected text into words and characters as an integ ral part of the recognition process, Their invariance to size, slant, and other transformations of the basic characters, and The ease with w hich contextual information and language models can be incorporated in to the recognition algorithms. We give a brief overview of OCR algorit hms based on two-dimensional hidden Markov models, and we present thre e case studies that show their remarkable strengths.