Document Image Decoding Using Markov Source Models

This paper describes a communication theory approach to document image recognition, patterned after the use of hidden Markov models in speech recognition. In general, a document recognition problem is viewed as consisting of three elements— an image generator, a noisy channel and an image decoder. A document image generator is a Markov source (stochastic finite-state automaton) that combines a message source with an imager. The message source produces a string of symbols, or text, that contains the information to be transmitted. The imager is modeled as a finite-state transducer that converts the one-dimensional message string into an ideal two-dimensional bitmap. The channel transforms the ideal image into a noisy observed image. The decoder estimates the message, given the observed image, by finding the a posteriori most probable path through the combined source and channel models using a Viterbi-like dynamic programming algorithm. The proposed approach is illustrated on the problem of decoding scanned telephone yellow pages to extract names and numbers from the listings. A finite-state model for yellow page columns was constructed and used to decode a database of scanned column images containing about 1100 individual listings. Overall, 99.5% of the listings were correctly recognized, with character classification rates of 98% and 99.6%, respectively, for the names and numbers. Keywords—document recognition, text recognition, image decoding, stochastic grammars, Markov sources