Hidden Markov Model Length Optimization forHandwriting Re ognition Systems

Hmm Based High Accuracy Off-line Cursive Handwriting Recognition by a Baseline Detection Error Tolerant Feature Extraction Approach

HANDWRITING RECOGNITION BY A BASELINE DETECTION ERROR TOLERANT FEATURE EXTRACTION APPROACH W. WANG, A. BRAKENSIEK, A. KOSMALA, G. RIGOLL Dept. of Computer S ien e, Fa ulty of Ele tri al Engineering Gerhard-Mer ator-University Duisburg Bismar kstr. 90, 47057 Duisburg, Germany E-mail: fwwwang, anja, kosmala, rigollg fb9-ti.uni-duisburg.de Hidden Markov Models (HMMs) an model the similarity and va...

Clustering Wide-Contexts and HMM Topologies for Spontaneous Spee h Re ognition

Off-line Handwriting Recognition Using Various Hybrid Modeling Techniques and Character N-grams

HYBRID MODELING TECHNIQUES AND CHARACTER N-GRAMS A. Brakensiek, J. Rottland, A. Kosmala, G. Rigoll Dept. of Computer S ien e, Fa ulty of Ele tri al Engineering Gerhard-Mer ator-University Duisburg 47057 Duisburg, Germany E-mail: fanja, rottland, kosmala, rigollg fb9-ti.uni-duisburg.de In this paper a system for o -line ursive handwriting re ognition is des ribed. The system is based on Hidden M...

Hidden Markov model length optimization for handwriting recognition systems

مدل یابی انتشار بیماری های عفونی بر اساس رویکرد آماری بیز

Background and Aim: Health surveillance systems are now paying more attention to infectious diseases, largely because of emerging and re-emerging infections. The main objective of this research is presenting a statistical method for modeling infectious disease incidence based on the Bayesian approach.Material and Methods: Since infectious diseases have two phases, namely epidemic and non-epidem...