This paper describes an adaptive feature extraction method that exploits category-specific information to overcome both image degradation and deformation in character recognition. When recognizing multiple fonts, geometric features such as directional information of strokes are often used but they are weak against the deformation and degradation that appear in videos or natural scenes. To tackle these problems, the proposed method estimates the degree of deformation and degradation of an input pattern by comparing the input pattern and the template of each category as category-specific information. This estimation enables us to compensate the aspect ratio associated with shape and the degradation in feature values and so obtain higher recognition accuracy. Recognition experiments using characters extracted from videos show that the proposed method is superior to the conventional alternatives in resisting deformation and degradation.

Most conventional methods used in character recognition extract geometrical features, such as stroke direction and connectivity, and compare them with reference patterns in a stored dictionary. Unfortunately, geometrical features are easily degraded by blurs and stains, and by the graphical designs such as used in Japanese newspaper headlines. This noise must be removed before recognition commences, but no preprocessing method is perfectly accurate. This paper proposes a method for recognizing degraded characters as well as characters printed on graphical designs. This method extracts features from binary images, and a new similarity measure, the complementary similarity measure, is used as a discriminant function; it compares the similarity and dissimilarity of binary patterns with reference dictionary patterns. Experiments are conducted using the standard character database ETL-2, which consists of machine-printed Kanji, Hiragana, Katakana, alphanumeric, and special characters. The results show that our method is much more robust against noise than the conventional geometrical-feature method. It also achieves high recognition rates of over 97% for characters with textured foregrounds, over 99% for characters with textured backgrounds, over 98% for outline fonts and over 99% for reverse contrast characters. The experiments for recognizing both the fontstyles and character category show that it also achieves high recognition rates against noise.

Skin tone detection in conditions where illuminate intensity and/or chromaticity can vary often comes with high computational time or low accuracy. Here a technique is presented integrating chromaticity and intensity normalization combined with a neural skin tone classification network to achieve robust classification faster than other approaches.

The difference is investigated between the effects of meaningless and meaningful noise on performance of a task, along with the relevance of the contents of the noise. The extent to which noise content affects task performance is measured and used to set environmental noise assessment. The use of meaningless noise to obscure meaningful noise is also investigated.