CONF vincia01c-conf/IDIAP Offline Cursive Word Recognition using Continuous Density Hidden Markov Models trained with PCA or ICA Features Vinciarelli, Alessandro Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2001/rr01-46-conf.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/vincia01c Related documents Proceedings of International Conference on Pattern Recognition III 81-84 2002 Quebec City (Canada) This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is a continuous density hiddden Markov model trained using either the raw data, or data transformed using Principal Component Analysis or Independent Component Analysis. Both techniques significantly improved the recognition rate of the system. Preprocessing, normalization and feature extraction are described in detail as well as the training technique adopted. Several experiments were performed using a publicly available database. The accuracy obtained is the highest presented in the literature over the same data. REPORT vincia01c/IDIAP Offline Cursive Word Recognition using Continuous Density Hidden Markov Models trained with PCA or ICA Features Vinciarelli, Alessandro Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2001/rr01-46.pdf PUBLIC Idiap-RR-46-2001 2001 IDIAP This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is a continuous density hiddden Markov model trained using either the raw data, or data transformed using Principal Component Analysis or Independent Component Analysis. Both techniques significantly improved the recognition rate of the system. Preprocessing, normalization and feature extraction are described in detail as well as the training technique adopted. Several experiments were performed using a publicly available database. The accuracy obtained is the highest presented in the literature over the same data.