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Related Experiment Video

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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

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The sensitivity of the modified viterbi algorithm to the source statistics.

R Shinghal¹, G T Toussaint

¹Department of Computer Science, Concordia University, Montreal, P.Q., Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

The modified Viterbi algorithm shows robustness in character recognition, even with differing text statistics. Performance remains stable despite significant variations in N-gram distributions between text sources.

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Area of Science:

Computational linguistics
Pattern recognition
Natural Language Processing

Background:

The modified Viterbi algorithm is widely used for incorporating contextual information in text recognition.
Previous research has not extensively explored the algorithm's robustness concerning source statistics.

Purpose of the Study:

To investigate the sensitivity of the modified Viterbi algorithm to variations in source statistics.
To assess the performance of character recognition systems using this algorithm under different statistical conditions.

Main Methods:

Experiments were conducted to evaluate the modified Viterbi algorithm's performance.
N-gram statistics were estimated from a source text (Source A).
The algorithm's performance was tested on a different text source (Source B) with potentially different statistical properties.

Main Results:

The character-recognition system incorporating the modified Viterbi algorithm did not show performance deterioration.
This stability was observed even when Source B had significantly different N-gram distributions or entropy compared to Source A.
The algorithm's performance was robust to changes in source statistics.

Conclusions:

The modified Viterbi algorithm demonstrates significant robustness with respect to source statistics in character recognition tasks.
Character recognition systems utilizing this algorithm can maintain performance levels despite variations in the statistical properties of the input text.
This finding supports the broader applicability of the modified Viterbi algorithm in diverse text recognition scenarios.