A Study on Recognition of Handwritten Character Images
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2014 by IJCTT Journal | ||
| Volume-12 Number-3 |
||
| Year of Publication : 2014 | ||
| Authors : Shri Praveenkumar Katigar | ||
| DOI : 10.14445/22312803/IJCTT-V12P124 |
Shri Praveenkumar Katigar."A Study on Recognition of Handwritten Character Images". International Journal of Computer Trends and Technology (IJCTT) V12(3):123-125, June 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
This document deals with study on recognition of handwritten character images.
References
[1] J. pradeep, E. Strinivasan, and S. Himavathi, Neural network based handwritten character recognition system with feature extraction.
[2] W. Wu and Y. Bao, Online handwritten magnolia words recognition based on multiple classifiers.
[3] C. L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, Handwritten digit recognition: investigation of normalization and feature extraction techniques, Pattern Recognition,
[4] C. Y. Suen, C. Nadal, R. Legault, T. A. Mai, and L. Lam, Computer recognition of unconstrained handwritten numerals,
[5] R. M. Brown, T. H. Fay, and C. L. Walker, Handprinted Symbol Recognition System, Pattern Recognition,
[6] B. T. Mittchell and A. M. Gillies, A Model-Based Computer Vision System for Recognizing Handwritten ZIP Codes, Machine Vision and Applications, vol. 21, iss.4, pp.231-243, 1989.
[7] Y. Tao, R. C. M. Lam, and Y. Y. Tang, Feature Extraction Using Wavelet and Fractal, Pattern Recognition Letters vol. 22, iss. 1, pp. 271-287, 2001.
[8] M. Shi, Y. Fujisawa, T. Wakbayashi, and F. Kimura, Handwritten Numeral Recognition Using Gradient and Curvature of Gray Scale Image, Patter Recognition, vol. 35, iss. 10, pp. 2051-2059, 2002.
[9] L. N. Teow and K. F. Loe, Robust Vision-Based Feature and Classification Schemes for Off-Line Handwritten Digit Recognition, Pattern Recognition, vol. 35, iss. 1, pp. 2355-2364, 2002.
[10] D. Decoste and B. Scholkopf, Training Invariant Support Vector Machines, Machine Learning, vol. 46, iss. 1-3, pp. 160-190, 2002.
[11] C. L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, Handwritten Digit Recognition Using State-of-the-art Techniques, Proceedings of the 8th International Workshop on Frontiers in Handwritten Recognition, Ontario, Canada, pp. 320-325, Aug. 2002.
[12] E. Kussul and T. Baidyk, Improved Method of Handwritten Digit Recognition Tested on MNIST Database, Image and Vision Computing, vol. 22, iss. 12, pp. 971-981, 2004.
[13] E. Kussul, T. Baidyk, and D. C. Wunsch II, Image Recognition Systems with Permutative Coding, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, pp. 1788-1793, Aug. 2005.
[14] J. X. Dong, Speed and Accuracy: Large-Scale Machine Learning Algorithms and Their Applications, Doctoral thesis, Computer Science Department, Concordia University, Montreal, Oct. 2003.
[15] E. Avci, D. Hanbay, and A. Varol, An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition. Expert Systems with Applications, vol.33, pp. 582-589, 2007.
[16] M. Hosoz, H. M. Ertunc, and H. Bulgurcu, An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower. Expert Systems with Applications, vol. 38, pp. 14148-14155, 2011.
[17] A. Keles, A. Keles, and U. Yavuz, Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Systems with Applications, vol.38, pp. 5719-5726, 2011.
Keywords
Hand written Characters, neural network, Fuzzy logic.