Abstract The importance of contextual information, at various different levels, for the satisfactory solution of pattern recognition problems is illustrated by examples. A tutorial survey of techniques for using contextual information in pattern recognition is presented. Emphasis is placed on the problems of image classification and text recognition, where the text is in the form of machine and handprinted characters, cursive script, and speech. The related problems of scene analysis, natural language understanding, and error-correcting compilers are only lightly touched upon.

Use of context in pattern recognition.

The distribution of frequency counts of distinct words by length in a language's vocabulary will be analyzed using two methods. The first, will look at the empirical distributions of several languages and derive a distribution that reasonably explains the number of distinct words as a function of length. We will be able to derive the frequency count, mean word length, and variance of word length based on the marginal probability of letters and spaces. The second, based on information theory, will demonstrate that the conditional entropies can also be used to estimate the frequency of distinct words of a given length in a language. In addition, it will be shown how these techniques can also be applied to estimate higher order entropies using vocabulary word length.

Distinct word length frequencies: distributions and symbol entropies

The goal of this paper is to show the dependency of measured entropy of English text on subject of the experiment, the type of English text, and the methodology used to estimate the entropy.

/pdf/entropy-of-english-text-experiments-with-humans-and-a-376haerewt.pdf

Entropy of English text: experiments with humans and a machine learning system based on rough sets

The concept of the entropy of natural languages, first introduced by Shannon [A mathematical theory of communications, Bell Syst. Tech. J.27, 379–423 (1948)] and its significance is discussed. A review of various known approaches to and results of previous studies of language entropy is presented. A new improved method for evaluation of both lower and upper bounds of the entropy of printed texts is developed. This method is a refinement of Shannon's prediction (guessing) method [Shannon, Prediction and entropy of printed English, Bell Syst. Tech. J.30, 54–64 (1951)]. The evaluation of the lower bound is shown to be a classical linear programming problem. Statistical analysis of the estimation of the bounds is given and procedures for the statistical treatment of the experimental data (including verification of statistical validity and sigficance) are elaborated. The method has been applied to printed Hebrew texts in a large experiment (1000 independent samples) in order to evaluate entropy and other information-theoretical characteristics of the Hebrew language. The results have demonstrated the efficiency of the new method: the gap between the upper and lower bounds of entropy has been reduced by a factor of 2.25 compared to the original Shannon approach. Comparison with other languages is given. Possible applications of the method are briefly discussed.

Entropy of natural languages: Theory and experiment

The first- second- and third-order entropies of written Arabic text are calculated. The calculated value of the third-order entropy shows that a redundancy of more than 50 percent is exhibited by the text.

First second- and third-order entropies of Arabic text (Corresp.)

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First second- and third-order entropies of Arabic text (Corresp.)