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Lomte, S.
- Review on Handwritten English Capital Letters Recognition Using Artificial Neural Network
Authors
1 Computer Technology Department of K. B. P. Polytechnic, Kopargaon, IN
2 Department of Computer Science & Engineering, Everest Educational Society's Group of Institutions, College of Engineering & Technology, Aurangabad (Maharashtra), IN
Source
Biometrics and Bioinformatics, Vol 6, No 8 (2014), Pagination: 201-204Abstract
Pattern recognition plays very important role in handwritten capital English letters. It will be covered development in the areas of engineering, artificial intelligence, statistics, computer science, psychology and physiology; image processing etc. from 1960.The goal of pattern recognition is to clarify these complicated mechanisms of decision-making processes and to automate these functions using computers. However, because of the complex Nature of the problem, most pattern recognition research has been concentrated on more realistic problems, such as the recognition of handwritten digits and the classification. Pattern matching and analysis of handwritten (PMAAOH) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic.
Pattern matching and analysis of handwritten (PMAAOH) is a active problem researchers had been research into this area for so long especially in the recent years. In my study there are many fields concern with English capital letters, for example, checks in banks or recognizing numbers in car plates, the subject of recognition appears. A system for recognizing isolated English capital letters will be as an approach for capital English letters with such application Among the different traditional approaches of pattern matching and recognition the statistical approach has been most intensively studied and used in real time practice. More commonly, the addition of artificial neural network techniques significant attention. The design of a handwritten capital English characters recognition system requires definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, classifier design and learning, selection of training and test samples, and performance evaluation.
