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Markov Models For Handwriting Recognition: An In-Depth Exploration

Quentin Powell

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Published in Markov Models For Handwriting Recognition (SpringerBriefs In Computer Science)

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Markov models are a type of statistical model that is used to represent sequential data. They are named after the Russian mathematician Andrey Markov, who first developed them in the early 1900s. Markov models have been used in a wide variety of applications, including speech recognition, natural language processing, and handwriting recognition.

Markov Models for Handwriting Recognition (SpringerBriefs in Computer Science)

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Handwriting recognition is the task of converting handwritten text into digital text. It is a challenging task, as there is a great deal of variation in the way that people write. However, Markov models can be used to capture the statistical regularities of handwriting, which can help to improve the accuracy of handwriting recognition systems.

How Markov Models Work

Markov models are based on the assumption that the current state of a system is only dependent on the previous state. This is known as the Markov property. In the case of handwriting recognition, the current state of the system is the identity of the current character. The previous state is the identity of the previous character.

Markov models are represented using a transition matrix. The transition matrix contains the probabilities of transitioning from one state to another. For example, the transition matrix for a two-state Markov model might look like this:

| | State 1 | State 2 | |---|---|---| | State 1 | 0.8 | 0.2 | | State 2 | 0.3 | 0.7 |

This transition matrix shows that there is an 80% chance of transitioning from State 1 to State 1 and a 20% chance of transitioning from State 1 to State 2. Similarly, there is a 30% chance of transitioning from State 2 to State 1 and a 70% chance of transitioning from State 2 to State 2.

Markov Models for Handwriting Recognition

Markov models can be used for handwriting recognition by modeling the sequence of characters in a handwritten text. The transition matrix of the Markov model can be estimated from a training set of handwritten text. Once the transition matrix has been estimated, the Markov model can be used to predict the next character in a handwritten text given the previous characters.

Markov models have been shown to be effective for handwriting recognition. However, they can be computationally expensive to train. In addition, Markov models can be sensitive to noise in the input data. This can lead to errors in the recognition of handwritten text.

Markov models are a powerful tool for handwriting recognition. They can capture the statistical regularities of handwriting, which can help to improve the accuracy of handwriting recognition systems. However, Markov models can be computationally expensive to train and can be sensitive to noise in the input data. These limitations should be considered when using Markov models for handwriting recognition.

References

Markov, A. A. (1906). Rasprostranenie zakona bol'shikh chisel na velichiny, zavisyashchie drug ot druga. Izvestiya Imperatorskoi Akademii Nauk, Seriya 5, 12(5),71-94.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2),257-286.
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Markov Models for Handwriting Recognition (SpringerBriefs in Computer Science)

5 out of 5