Learn More
This paper describes an off-line segmentation-free handwritten Arabic words recognition system. The described system uses discrete HMMs with explicit state duration of various kinds (Gauss, Poisson and Gamma) for the word classification purpose. After preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of(More)
In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic information we make a sampling operation by the consideration of(More)
To determine whether changes in LH and testosterone (T) blood levels and pulse signals were induced by sexual arousal, nine healthy young males were presented on two different days with a sexually arousing (S) and a sexually neutral control (C) film. On both sessions, blood was sampled every 10 min for 12 hr. The Cluster and the Detect pulse identification(More)
We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive(More)