Handwritten address recognition with open vocabulary using character n-grams

Anja Brakensiek, Jörg Rottland, Gerhard Rigoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

In this paper a recognition system, based on tied-mixture hidden Markov models, for handwritten address words is described, which makes use of a language model that consists of backoff character n-grams. For a dictionary-based recognition system it is essential that the structure of the address (name, street, city) is known. If the single parts of the address cannot be categorized, the used vocabulary is unknown and thus unlimited. The performance of this open vocabulary recognition using n-grams is compared to the use of dictionaries of different sizes. Especially, the confidence of recognition results and the possibility of a useful post-processing are significant advantages of language models.

Original languageEnglish
Title of host publicationProceedings - 8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
Pages357-362
Number of pages6
DOIs
StatePublished - 2002
Event8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002 - Ontario, ON, Canada
Duration: 6 Aug 20028 Aug 2002

Publication series

NameProceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR
ISSN (Print)1550-5235

Conference

Conference8th International Workshop on Frontiers in Handwriting Recognition, IWFHR 2002
Country/TerritoryCanada
CityOntario, ON
Period6/08/028/08/02

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