Pattern Recognition

Abstract

Pattern recognition is the action taken on raw data based on the category of pattern. It is the field of automatic discoveries of regularities in the input raw data by using statistical techniques or machine learning algorithms. These regularities are then used to take decision actions for classifying the data in different categories. When a fundamental equation does not describe the pattern, pattern recognition analyses the behaviour of the non-linear complex systems. A pattern is represented by vector feature values where features are represented by discrete, continuous, or binary variables. Pattern recognition has applications in fast-emerging areas like biometrics, bioinformatics, multimedia data analysis, machine learning, and data science, and it supports fields like computer vision, image processing, text and document analysis, and neural networks. There are two pattern recognition techniques. First is supervised, which is used for classification and regression; second is unsupervised, which is used for clustering. A few examples of pattern recognition are speech recognition, optical character recognition (OCR), multimedia document recognition (MDR), sentiment analysis, etc. Pattern recognition consists of multiple steps like data collection, data cleaning, features selection and extraction, discover regularities, group regularities in segments, analyse segments, and implement insights by examining similarities among data.