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    Pattern Recognition with Machine Learning

    Generally, recognition is identifying a person or a thing by past experience and knowledge. Pattern recognition is a branch of machine learning that focuses on recognizing patterns and regularities in data [1]. Its aim is the classification of objects into categories or classes. It is an important part of most intelligence systems built for decision-making [2]. There are different types of Pattern recognition some of which are mentioned below.

    Biometric Recognition

    Biometric recognition is done by evaluating one or more distinguishing biological characteristics by which a person can be uniquely identified. Unique identifiers include fingerprints, hand geometry, earlobe geometry, retina and iris patterns, voice waves, and signatures [2]. These are some biometric recognition systems mentioned below.

    • Fingerprint recognition
    • Handwritten biometric recognition
    • Iris recognition
    • Facial recognition
    Textual Recognition

    Text Recognition is possible by receiving and interpreting handwritten input from sources such as paper documents, web pages, photographs, touch screens and other devices. The image of the written text may be sensed offline from a piece of paper by optical scanning. It involves the conversion of handwritten or printed text into machine-encoded text [3]. Text Recognition is also done by capturing some interesting features from the text.  These are some textual recognition systems mentioned below.

    • Handwriting recognition
    • Magnetic ink character recognition
    • Handwriting recognition
    • Magnetic ink character recognition
    • Optical character recognition
    Linguistic Recognition

    We speak of linguistic pattern recognition when the set of outputs is structured linguistically. It includes problems of determining which natural language given content is in, like natural language processing, language identification, or language guessing [3]. These are some linguistic recognition systems mentioned below.

    • Natural language understanding
    • Speech recognition
    • Language identification
    Blog By:
    Noor Ul Huda, Jr. Project Manager
    1. Bishop, Christopher M. Pattern Recognition and Machine Learning, 2006 
    2. S. Theodoridis, K. Koutroumbas, Pattern Recognition, Elsevier(USA),1982

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