A Review of Pattern Recognition Using Statistics

Abstract

Abstract: Pattern recognition's primary objective is supervised or classification techniques. The statistical method has been the most intensively researched and implemented in practice among the various frameworks in which classification technique has typically been developed. In recent times, neural network techniques and methods based on statistical learning theory have gotten a lot of attention. The following issues must be carefully considered when designing a recognition system: pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design, learning, training, test sample selection, and performance evaluation. The overall challenge of detecting intricate forms with modifiable direction, position, and size has not been addressed despite just Fifty years of intensive research. Data mining, web browsing, recovery of multimedia content, face recognition, and cursive handwriting recognition are just a couple of minor new and emerging applications that require strong and fast pattern recognition methods. The intention of this review paper is to summarize and analyze some of the adopted approaches in various phases of a recognition system, as well as to identify areas of research and applications that are at the forefront of innovation in this exciting and challenging field.