Abstract
In this paper we present IAM-OnDB - a new large online handwritten sentences database. It is publicly available and consists of text acquired via an electronic interface from a whiteboard. The database contains about 86 K word instances from an 11 K dictionary written by more than 200 writers. We also describe a recognizer for unconstrained English text that was trained and tested using this database. This recognizer is based on hidden Markov models (HMMs). In our experiments we show that by using larger training sets we can significantly increase the word recognition rate. This recognizer may serve as a benchmark reference for future research.
Keywords
Computer scienceHidden Markov modelArtificial intelligenceWhiteboardBenchmark (surveying)Natural language processingSentenceWord (group theory)Speech recognitionHandwriting recognitionFeature extractionMultimedia
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Publication Info
- Year
- 2005
- Type
- article
- Pages
- 956-961 Vol. 2
- Citations
- 210
- Access
- Closed
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Cite This
Marcus Liwicki,
Horst Bunke
(2005).
IAM-OnDB - an on-line English sentence database acquired from handwritten text on a whiteboard.
Eighth International Conference on Document Analysis and Recognition (ICDAR'05)
, 956-961 Vol. 2.
https://doi.org/10.1109/icdar.2005.132
Identifiers
- DOI
- 10.1109/icdar.2005.132