Open Access Open Access  Restricted Access Subscription or Fee Access

Review On Handwritten English Capital Letters Recognition Using Artificial Neural Network

H.E. Khodke, Dr. S. Lomte, R. Auti, Neha- KhatriValmik, Seema Singh


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.


Introduction, Related Work, Neural Network, Proposed System.

Full Text:



Buffa F., Porceddu I. 1997”Temperature forecast and dome seeing minimization. A case study using neural network model ‘

Claus D.”Handwritten Digit Recognition ‘, ~dclaus.

MALOTHU NAGUA,N VIJAY SHANKAR,K.ANNAPURNA," novel method for Handwritten Digit Recognition with Neural Networks”, International Journal of Computer Science and Information Technologies, Vol. 2 (4) , 2011, 1685-1692.

Prof. S.P.Kosbatwar, Prof.S.K.Pathan,: “Pattern Association for character recognition by Back-Propagation algorithm using Neural Network approach” International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.1, February 2012.

Handwritten Optical character recognition / Optical_character_recognition

Kauleshwar Prasad, Devvrat C. Nigam, Ashmika Lakhotiya:Character Recognition Using Matlab‟s Neural Network Toolbox,International Journal of u- and e- Service, Science and Technology Vol. 6, No. 1, February, 2013.

Reetika Verma,Mrs. Rupinder Kaur,"Review on Offline Handwritten Character Recognition using Feed Forward Neural Network and SURF Feature",International Journal of Advanced Research in Computer and Communication EngineeringVol. 3, Issue 5, May 2014

Gonzalez, R.C., and Woods, R.E., 2004. Digital Image Processing (2nd edition), Pearson Education.

Pranob K Charles,V.Harish,M.Swathi,CH. Deepthi, “A Review on the Various Techniques used for Optical Character Recognition”, in International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 2, Issue 1, Jan-Feb 2012, pp.659-662

MALOTHU NAGU, N VIJAY SHANKAR,K.ANNAPURNA, "A novel method for Handwritten Digit Recognition with Neural Networks”, in International Journal of Computer Science and Information Technologies, Vol. 2 (4) , 2011, 1685-1692

K.Venkata Reddy, D.Rajeswara Rao, U.Ankaiah, K.Rajesh,"Handwritten Character Digit Recognition Using Artificial Neural Networks",International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 4, April 2013.

M. S. Ryan and G. R. Nudd.,"Dynamic Character Recognition Using Hidden Markov Models.",Department of Computer Science, University of Warwick, Coventry, CV4 7AL, England(1993).

Robert, Jain and Mao, “Statistical pattern recognition: A Review", IEEE transaction on machine learning and pattern analysis, VOL.22. NO.1. January 2000.

Sameeksha Barve,"Optical Character Recognition Using Artificial Neural Network", ISSN: 2278 – 1323International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.