A Survey on Genetic Algorithm Based Classification Technique for Handwritten Character Recognition
Abhishek Phukan and Mrinaljit Borah. Article: A Survey on Genetic Algorithm Based Classification Technique for Handwritten Character Recognition. IJCA Proceedings on National Conference cum Workshop on Bioinformatics and Computational Biology NCWBCB(2):1-4, May 2014. Full text available. BibTeX
@article{key:article,
author = {Abhishek Phukan and Mrinaljit Borah},
title = {Article: A Survey on Genetic Algorithm Based Classification Technique for Handwritten Character Recognition},
journal = {IJCA Proceedings on National Conference cum Workshop on Bioinformatics and Computational Biology},
year = {2014},
volume = {NCWBCB},
number = {2},
pages = {1-4},
month = {May},
note = {Full text available}
}
Abstract
The paper depicts the progress achieved in the field of character recognition using genetic algorithm. Character recognition is a process in image processing where the characters fed into the system are identified and classified. The main focus of this paper is on the offline character recognition since very less work has been done in this field. The use of genetic algorithm is the basis of this paper and it focuses on the advantages of using a genetic algorithm and also a survey of the works that have been implemented so far.
References
- Nafiz Arica, Fatos T. Yarman-Vural, "An overview of character recognition focused on off-line handwriting"
- SandeepSaha, Nabarag Paul, Sayam Kumar Das, SandipKundu, "optical character recognition using 40-point feature extraction and Neural Network"
- Gaurav Y. Tawde, Mrs. Jayashree M. Kundargi, " an overview of feature extraction techniques in OCR for indian scripts focused on offline handwriting "
- Pulak Pukait, "9th North-East Workshop on computational information processing"
- Mrs. C. Mythili, Dr. V. Kavitha, "efficient technique for color Image noise reduction"
- A. Cheung, M. Bennamoun, N. W. Bergmann, "an Arabic optical character recognition system using recognition based segmentation"
- Ms. SnehalDalal, Mrs. Latesh Malik, "A survey for feature extraction methods in handwritten script identification"
- VedguptSaraf, D. S. Rao, "Devnagiri script character recognition using genetic algorithm for better efficiency"
- Pier Luca Lanzi, Politecnico di Milano, "Fast feature selection with genetic algorithm: A filter approach"
- A. K. Jain, J. Mao, and K. M. Mohiuddin, "Artificial Neural Networks:A Tutorial", IEEE Computer, pp. 31-44, 1996.
- VedPrakashAgnihotri, "offline handwritten Devanagiri script recognition"
- ChomtipPornpanomchai, VerachadWongsawangtham, SatheanpongJeungudomporn, NannaphatChatsumpun, " Thai Handwritten Recognition by gentic algorithm (THCRGA)"
- E. K. Vellingiriraj, P. Balasubramanie, "Recognition of ancient Tamil handwritten characters in palm manuscripts using genetic algorithm"
- ShashankMathur, "self-evolving character recognition using genetic operators"
- Lu H. , Sung S. Y. and Lu Y. : On Preprocessing Data for EffectiveClassification. Workshop on Research Issue on Data Miningand Knowledge Discovery in Databases. (1996)
- Richeldi M. , Rossotto M. : Supervised Quantization of ContinuousPredictor Variables. Seminars on New Techniques and Technologyfor Statistics. Bonn 20-22 November. (1995)
- Dietterich T. G. : Statistical Tests for Comparing SupervisedClassification Learning Algorithms. Tech. Report. Department ofComputer Science. Oregon State University. (1996)
- Grefenstette J. J. : Technical Report CS-83-11 ComputerScienceDept. , Vanderbilt Univ.