HIGH PERFORMANCE UNCONSTRAINED WORD RECOGNITION SYSTEM COMBINING HMMs AND MARKOV RANDOM FIELDS

1997 ◽  
pp. 309-326 ◽  
Author(s):  
GEORGE SAON ◽  
ABDEL BELAÏD
Keyword(s):  
Word Recognition ◽  
Random Fields ◽  
Markov Random Fields ◽  
High Performance ◽  
Recognition System ◽  
Markov Random

High Performance Unconstrained Word Recognition System Combining HMMs and Markov Random Fields

1997 ◽  
Vol 11 (05) ◽  
pp. 771-788 ◽  
Author(s):  
George Saon ◽  
Abdel Belaïd
Keyword(s):  
Word Recognition ◽  
Random Fields ◽  
Markov Random Fields ◽  
High Performance ◽  
Recognition Rate ◽  
Recognition System ◽  
State Observation ◽  
Markov Random ◽  
Word Images ◽  
Column Product

In this paper we present a system for the recognition of handwritten words on literal check amounts which advantageously combine HMMs and Markov random fields (MRFs). It operates at pixel level, in a holistic manner, on height normalized word images which are viewed as random field realizations. The HMM analyzes the image along the horizontal writing direction, in a specific state observation probability given by the column product of causal MRF-like pixel conditional probabilities. Aspects concerning definition, training and recognition via this type of model are developed throughout the paper. We report a 90.08% average word recognition rate on 2378 words and a 79.52% amount rate on 579 amounts of the SRTP* French postal check database (7031 words, 1779 amounts, different scriptors).

Sampling from Gaussian Markov random fields conditioned on linear constraints

2008 ◽  
Vol 48 ◽  
pp. 1041 ◽  
Author(s):  
Daniel Peter Simpson ◽  
Ian W. Turner ◽  
A. N. Pettitt
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Linear Constraints ◽  
Gaussian Markov Random Fields ◽  
Markov Random

scholarly journals A Methodology for Redesigning Networks by Using Markov Random Fields

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1389
Author(s):  
Julia García Cabello ◽  
Pedro A. Castillo ◽  
Maria-del-Carmen Aguilar-Luzon ◽  
Francisco Chiclana ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Random Fields ◽  
Markov Random Fields ◽  
Application Performance ◽  
Cross Border ◽  
Markov Random ◽  
User Groups ◽  
Universal Definition ◽  
Definition Of ◽  
Decision Making Model

Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.

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