Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/105800
Título: An Investigation of Discrete Hidden Markov Models on Handwritten Short Answer Assessment System
Autores/as: Suwanwiwat, Hemmaphan
Dasb, Abbhijit
Ferrer Ballester, Miguel Ángel 
Pal, Umapada
Blumenstein, Michael
Clasificación UNESCO: 3325 Tecnología de las telecomunicaciones
Palabras clave: off-line automatic assessment system
Hidden Markov Models (HMMs)
fixed-point arithmetic
geometric features
Fecha de publicación: 2018
Conferencia: )
Resumen: This paper presents an investigation of an off-line automatic assessment system utilising discrete Hidden Markov Models. A set of geometric features were extracted from handwritten words and were later classified by HMMs. There were two training datasets employed in the experiments; the first training dataset contained all correct answers to the questions whereas another training dataset contained both correct and incorrect answers to the questions. Datasets contained 3,000 and 3,400 handwritten samples, respectively. The experiments yielded promising results whereby the highest recognition rate of 91.90% with a 100% accuracy was achieved on our database.
URI: http://hdl.handle.net/10553/105800
ISBN: 1-895193-06-0
Fuente: Proceedings of 1st International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI 2018)
Colección:Actas de congresos
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