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Offline Recognition of Large Vocabulary Cursive Handwritten Text
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper describes a global model designed to jointly detect and recognize a street name within a delivery line of an handwritten address block image. The model used is based on Hidden Markov Models (HMM). The lines are firstly preprocessed, then segmented and characterized by two types of features. We create a HMM for each street name by simply concatenating the corresponding letter models, elementary HMM learned on a large city name database. A street name may often be surrounded by other information on the left and on the right within the delivery line. These phenomena are roughly modelled by trigrams. The global model is then simply obtained by the concatenation of trigram models with the HMMs corresponding to street names.