{'fraction_non_alphanumeric': 0.012154696132596685, 'fraction_numerical': 0.0033149171270718232, 'mean_word_length': 5.4714285714285715, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Objective Evaluation of Wrinkle Recovery

A device for wrinkle recovery measurements involves the installation of a laser sensor. The shapes of fabric wrinkles are described by magnitude and sharpness using power spectra obtained from two-dimensional fast Fourier transformation and the distribution of fabric surface angles obtained from spline interpolation and vector operation. For the objective evaluation of wrinkle recovery, artificial neural networks are used rather than the AATCC subjective evaluation method. Five kinds of input patterns are categorized from the characterizations of wrinkle shape, and these are supplied to the neural networks for evaluation. Objective results from the neural networks are compared with subjective ones from human experts. The relationship is good with a correction coefficient of 0.95, but wrinkle rank depends somewhat on the color or pattern of the fabric.