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The work presents an extension of the fuzzy approach to 2-D shape recognition [1] through refinement of initial or coarse classification decisions under a two pass approach. In this approach, an unknown pattern is classified by refining possible classification decisions obtained through coarse classification of the same. To build a fuzzy model of a pattern class horizontal and vertical fuzzy partitions on the sample images of the class are optimized using genetic algorithm. To make coarse classification decisions about an unknown pattern, the fuzzy representation of the pattern is compared with models of all pattern classes through a specially designed similarity measure. Coarse classification decisions are refined in the second pass to obtain the final classification decision of the unknown pattern. To do so, optimized horizontal and vertical fuzzy partitions are again created on certain regions of the image frame, specific to each group of similar type of pattern classes. It is observed through experiments that the technique improves the overall recognition rate from 86.2%, in the first pass, to 90.4% after the second pass, with 500 training samples of handwritten digits.

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A smart society is an empowered society, which can improve the lives of its citizens by using the latest innovations and technologies. This improvement can happen in several dimensions out of which security is a major one. Inconsistency and forgery are very common phenomenon where handwritten signatures are often preserved for training a classifier to authenticate a person. The removal of outliers, at the outset, obviously improves the quality of training and the classifier. The present article deals with the mechanized segregation of the poor-quality authentic signatures from reliable ones. Machine learning algorithms for outlier handling utilizing clustering, classification and statistical techniques have been implemented in this context. Subsequent performance evaluation after outlier removal reflects improvement of both true positive and true negative recognition rate accuracy. The performance evaluation presents the significant differences between authentication accuracy and forgery accuracy in the context of building a safe, secure and smart society.

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