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��what is a pattern a pattern is essentially an arrangement or an ordering in which some organization of underlying structure can be said to exist we can view the world as made up of patterns watanabe defines a pattern as an entity vaguely defined that could be given a name a pattern can be referred to as a quantitative or structural description of an object or some other item of interest a set of patterns that share some common properties can be regarded as a pattern class the subject matter of pattern recognition by machine deals with techniques for assigning patterns to their respective classes automatically and with as little human intervention as possible for example the machine for automatically sorting mail based on digit zip code at the post office is required to recognize numerals in this case there are ten pattern classes one for each of the digits the function of the zip code recognition machine is to identify geometric patterns each representing an input digit as being a member of one of the available pattern classes a pattern can be represented by a vector composed of measured stimuli or attributes derived from measured stimuli and their interrelationships often a pattern is characterized by the order of elements of which it is made rather than the intrinsic nature of these elements broadly speaking pattern recognition involves the partitioning or assignment of measurements stimuli or input patterns into meaningful categories it naturally involves extraction of significant attributes of the data from the background of irrelevant details speech recognition maps a waveform into words in character recognition a matrix of pixels or strokes is mapped into characters and words other examples of pattern recognition include signature verification recognition of faces from a pixel map and friend or foe identification likewise a system that would accept sonar data to determine whether the input was a submarine or a fish would be a pattern recognition system pattern recognition systems for a typical pattern recognition system the determination of the class is only one of the aspects of the overall task in general pattern recognition systems receive data in the form of raw measurements which collectively form a stimuli vector uncovering relevant attributes in features present within the stimuli vector is typically an essential part of such systems in some cases this may be all that is required an ordered collection of such relevant attributes which more faithfully or more clearly represent the underlying structure of the pattern is assembled into a feature vector class is only one of the attributes that may or may not have to be determined depending on the nature of the problem the attributes may be discrete values boolean entities syntactic labels or analog values learning in this context amounts to the determination of rules of associations between features and attributes of patterns practical image recognition systems generally contain several stages in addition to the recognition engine itself before moving on to focus on neural network recognition engines we will briefly describe a somewhat typical recognition system chen components of a pattern recognition system figure shows all the aspects of a typical pattern recognition task preprocessing partitions the image into isolated objects i e characters etc in addition it may scale the image to allow a focus on the object feature extraction abstracts high level information about individual patterns to facilitate recognition the classifier identifies the category to which the pattern belongs or in general the attributes associated with the given pattern the context processor increases recognition accuracy by providing relevant information regarding the environment surrounding the object for example in the case of character recognition it could be the dictionary and or language model support figure shows the steps involved in the design of a typical pattern recognition system the choice of adequate sensors preprocessing techniques and decision making algorithm is dictated by the characteristics of the problem domain unlike the expert systems the domain specific knowledge is implicit in the design and is not represented by a separate module a flow chart of the process of designing a learning machine for pattern recognition a pattern classification system is expected to perform supervised classification where a given pattern has to be identified as a member of already known or defined classes or unsupervised classification or clustering where a pattern needs to be assigned to a so far unknown class of patterns pattern recognition may be static or dynamic in the case of asynchronous systems the notion of time or sequential order does not play any role such a paradigm can be addressed using static pattern recognition image labeling understanding falls into this category in cases of dynamic pattern recognition where relative timing is of importance the temporal correlations between inputs and outputs may a major role the learning process has to determine the rules governing these temporal correlations this category includes such applications as control using artificial neural networks or forecasting using neural nets in the case of recognizing handwritten characters for example the order in which strokes emerge from a digitizing tablet provides much information that is useful in the recognition process the task of pattern recognition may be complicated when classes overlap see figure in this case the recognition system must attempt to minimize the error due to misclassification the classification error is significantly influenced by the number of samples in the training set several researchers for example see jain and chandrasekaran fukunaga and hayes foley have addressed this issue two categories of patterns plotted in the pattern space patterns belonging to both classes can be observed in the overlapping region copyright copy use 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