ESMERALDA is a development environment for statistical recognizers operating on sequential data (speech, handwriting, biological sequences). It supports continuous density Hidden Markov models, Markov chain (N-gramm) models, and Gaussian mixture models.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Fichtenberger, Hendrik; Gillé, Marc; Schmidt, Melanie; Schwiegelshohn, Chris; Sohler, Christian: BICO: BIRCH meets coresets for (k)-means clustering (2013)
- Plötz, Thomas; Fink, Gernot A.: Markov models for offline handwriting recognition: a survey (2009) ioport
- Faubel, Christian; Schöner, Gregor: Learning to recognize objects on the fly: a neurally based dynamic field approach (2008) ioport
- Plötz, Thomas; Fink, Gernot A.: Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs (2006)
- Wienecke, Markus; Fink, Gernot A.; Sagerer, Gerhard: Toward automatic video-based whiteboard reading (2005) ioport