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.

References in zbMATH (referenced in 5 articles )

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  1. Fichtenberger, Hendrik; Gillé, Marc; Schmidt, Melanie; Schwiegelshohn, Chris; Sohler, Christian: BICO: BIRCH meets coresets for (k)-means clustering (2013)
  2. Plötz, Thomas; Fink, Gernot A.: Markov models for offline handwriting recognition: a survey (2009) ioport
  3. Faubel, Christian; Schöner, Gregor: Learning to recognize objects on the fly: a neurally based dynamic field approach (2008) ioport
  4. Plötz, Thomas; Fink, Gernot A.: Pattern recognition methods for advanced stochastic protein sequence analysis using HMMs (2006)
  5. Wienecke, Markus; Fink, Gernot A.; Sagerer, Gerhard: Toward automatic video-based whiteboard reading (2005) ioport