One of the most important capabilities of mankind is learning by experience, by our endeavors, by our faults. By the time we attain an age of five most of us are able to recognize digits, characters; whether it is big or small, uppercase or lowercase, rotated, tilted. We will be able to recognize, even if the character is on a mutilated paper, partially occluded or even on the clustered background. Looking at the history of the human search for knowledge, it is clear that humans are fascinated with recognizing patterns in nature, understand it, and attempt to relate patterns into a set of rules. Informally, a pattern is defined by the common denominator among the multiple instances of an entity. Therefore, Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. Pattern recognition is concerned with the design and development of systems that recognize patterns in data. The purpose of a pattern recognition program is to analyze a scene in the real world and to arrive at a description of the scene which is useful for the accomplishment of some task.
Introduction of Pattern Recognition
Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. Pattern recognition deals with identifying a pattern and confirming it again. In general, a pattern can be a fingerprint image, a handwritten cursive word, a human face, a speech signal, a bar code, or a web page on the Internet.
The term pattern recognition refers to the task of placing some object to a correct class based on the measurements about the object. Usually this task is to be performed automatically with the help of computer. Objects to be recognized, measurements about the objects, and possible classes can be almost anything in the world.
Definition of Pattern Recognition
Pattern recognition can be defined as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data from a background of irrelevant detail. The field of pattern recognition is concerned mainly with the description and analysis of measurements taken from physical or mental processes. It consists of acquiring raw data and taking actions based on the “class” of the patterns recognized in the data. Earlier it was studied as a specialized subject due to higher cost of the hardware for acquiring the data and to compute the answers. The fast developments in computer technology and resources enhanced possible various practical applications of pattern recognition, which in turn contributed to the demands for further theoretical developments.
“Pattern recognition is the process of classifying input data into objects or classes based on key features.”
Pattern recognition is the science for observing, distinguishing the patterns of interest, and making correct decisions about the patterns or pattern classes. Thus, a biometric system applies pattern recognition to identify and classify the individuals, by comparing it with the stored templates.
Pattern recognition technique extracts a random pattern of human trait into a compact digital signature, which can serve as a biological identifier. The biometric systems use pattern recognition techniques to classify the users and identify them separately. Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can partially or entirely be implemented on computers.
The components of pattern recognition are as follows –
Pattern Recognition Techniques
Patterns generated from the raw data depend on the nature of the data. Patterns may be generated based on the statistical feature of the data. In some situations, underlying structure of the data decides the type of the pattern generated. In some other instances, neither of the two situation exits. In such scenarios a system is developed and trained for desired responses. Thus, for a given problem one or more of these different approaches may be used to obtain the solution. Hence, to obtain the desired attributes for a pattern recognition system, there are many different mathematical techniques. The four best-known approaches for the pattern recognition are:
Template Matching: Objects are directly compared with a few stored examples or prototypes that are representative of the underlying classes. Because of the large variations often encountered in these examples, the template matching is not the most effective approach to pattern recognition.
Geometrical Classification: Classes are represented by regions in the representation space defined by simple functions such that the training examples are classified as correctly as possible. Suppose, the average value of (height, weight) of women is (5’5”, 125 lb) and that of men is (5’11”, 157lb). A simple geometric woman vs. man classifier using (height, weight) as a two-dimensional representation may simplistically divide the representation space into two triangular regions. So, a person with (height, weight) = (5’2”, 122lb) will classified by this classifier as a woman.
Statistical Classification: Continuing with the foregoing example, a statistical classifier may estimate the statistical distribution of the two features, namely, height and weight of the two classes of interest (women and men) from known samples. At any coordinate or point in the representation space, one could estimate the likelihoods of it being a man or a woman; depending upon which likelihood is higher, one could determine the class of an entity. This method differs from the geometrical method in that the classes are not (pre)defined in terms of any regular shapes in the representation space.
Syntactic or Structural Matching: The height and weight representation space is too simplistic and it is conceivable that a person’s body shape is a better representation for determining his/her gender. One could decompose the shape of a person into component parts and describe the shape in terms of component parts and their relationships (e.g., how they are attached to each other).
Artificial Neural Networks: These networks attempt to apply the models of biological neural systems to solve practical pattern recognition problems. This approach has become so popular that the use of neural networks for solving pattern recognition problems has become an area on its own, and is often studied outside the biological context.
Pattern recognition is used in any area of science and engineering that studies the structure of observations. It is now frequently used in many applications in manufacturing industry, health care and military. Examples include:
- Optical character recognition (OCR) is becoming an integral part of document scanners, and is also used frequently in banking and postal applications. Printed characters can now be accurately recognized, and the improving performance of automatic recognition of handwritten cursive characters has diminished significantly the need of human interaction for OCR tasks.
- Automatic speech recognition is very important for user interaction with machines. Commercial systems for automatic response to flight queries, telephone directory assistance and telebanking are available. Often the systems are tuned to a specific speaker for better recognition accuracy.
- Computer vision deals with the recognition of objects as well as the identification and localization of their three-dimensional environments. This capability is required, for example, by robots to operate in dynamic or unknown environments. This can be useful from applications ranging from manufacturing to household cleaning, and even for rescue missions.
- Personal identification systems that use biometrics are very important for security applications in airports, ATMs, shops, hotels, and secure computer access. Recognition can be based on face, fingerprint, iris or voice, and can be combined with the automatic verification of signatures and PIN codes.
 “Chapter 1: Introduction To Pattern Recognition System”, available online at: http://shodhganga.inflibnet.ac.in/bitstream/10603/25143/7/07_chapter%201.pdf
 Vinita Dutt, Vikas Chaudhary and Imran Khan, “Pattern Recognition: an Overview”, American Journal of Intelligent Systems 2012, 2(1): pp. 23-27.
 M. P. Raj, “Applications of Pattern Recognition Algorithms in Agriculture: A Review”, 2015.