Fractal-based analysis of sign language

Abstract Two theoretical theorems are formulated for an approximative calculation of the Hausdorff dimension to a fractal model determined by time series as the reciprocal value of an arithmetic mean of the numerically calculated and statistically verified Hurst exponents on given scaling levels. After providing relevant details from quantitative linguistics, these theorems are applied to a concrete sign language text. Our experiment so relies on the fractal analysis of a Czech sign language text consisting of 74 sentences, 247 clauses and 893 signs, i.e. on three scaling levels. Its results are discussed in terms of long-time memory (autocorrelation) effects, usually called as persistence.

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