๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Recognition of degraded characters using dynamic Bayesian networks

โœ Scribed by Laurence Likforman-Sulem; Marc Sigelle


Publisher
Elsevier Science
Year
2008
Tongue
English
Weight
857 KB
Volume
41
Category
Article
ISSN
0031-3203

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Dynamic Bayesian networks for multi-band
โœ Khalid Daoudi; Dominique Fohr; Christophe Antoine ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 292 KB

This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time-frequency domain using the formalism of dynamic Bayesian networ

Character recognition using Fourier desc
โœ Ian P. Morns; Satnam S. Dlay ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 887 KB

A new neural network is presented for pattern recognition tasks. This new network., called the Dynamic Supervised Forward-Propagation Network (DSFPN), although based upon the unsupervised Counterpropagation Network (CPN), trains using a supervised aIgorithm. In addition it allows unsupervised dynami

An HMM-based character recognition netwo
โœ Hang Joon Kim; Sang Kyoon Kim; Kyung Hyun Kim; Jong Kook Lee ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 964 KB

In this paper, we propose a novel recognition model of on-line cursive Korean characters using the hidden Markov model (HMM) and a level building algorithm. The model is constructed as a form of recognition network with HMMs for graphemes and Korean combination rules. Though the network represents t