Publication

Video Text Recognition Based on Markov Random Field and Grayscale Consistency Constraint

2002
Reports

Abstract

A method for segmenting and recognizing text embedded in video and images is proposed in this paper. In the method, multiple segmentation hypotheses of text image are first generated based on a MRF model. Background regions in each hypothesis are then removed by using grayscale consistency constraint (GCC) in a connected component analysis procudure before being processed by an optical character recognition (OCR) software.

Official source

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2002
Reports

Abstract

Official source

Related concepts (23)

Markov random field

In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. The concept originates from the Sherrington–Kirkpatrick model. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.

Conditional random field

Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account. To do so, the predictions are modelled as a graphical model, which represents the presence of dependencies between the predictions. What kind of graph is used depends on the application.

Markov model

In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.

Related publications (1)

Video Text Recognition Based on Markov Random Field and Grayscale Consistency Constraint

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