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A Review of Handwritten Character Recognition
by
Nikita Mehta,
Jyotika Doshi
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 165 - Number 4 |
| Year of Publication: 2017 |
| Authors: Nikita Mehta, Jyotika Doshi |
10.5120/ijca2017913855
|
Nikita Mehta, Jyotika Doshi . A Review of Handwritten Character Recognition. International Journal of Computer Applications. 165, 4 ( May 2017), 37-40. DOI=10.5120/ijca2017913855
@article{
10.5120/ijca2017913855,
author = {
Nikita Mehta,
Jyotika Doshi
},
title = { A Review of Handwritten Character Recognition },
journal = {
International Journal of Computer Applications
},
issue_date = { May 2017 },
volume = { 165 },
number = { 4 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = {
37-40
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume165/number4/27565-2017913855/
},
doi = { 10.5120/ijca2017913855 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:11:33.900281+05:30
%A Nikita Mehta
%A Jyotika Doshi
%T A Review of Handwritten Character Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 4
%P 37-40
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The aim behind Optical Character Recognition is to create human like perception and character identification by artificial systems. A lot of work has been done for printed and handwritten character recognition for many languages across the world. Even for many Indian languages, a good amount of work is done, but it could not get that accuracy as English, Germen etc. languages because of its complexities. In this paper various techniques for Handwritten Character Recognition (HCR) are reviewed and analyzed.
References
- Desai, A. A. (2015). Support vector machine for identification of handwritten Gujarati alphabets using hybrid feature space. CSI Transactions on ICT, 2(4), 235-241.
- Thaker, H. R., & Kumbharana, C. K. (2014). Structural Feature Extraction to recognize some of the Offline Isolated Handwritten Gujarati Characters using Decision Tree Classifier. International Journal of Computer Applications,99(15), 46-50.
- Modi, M., Macwan, F., & Prajapati, R. Gujarati Character Identification: A Survey
- Magare, S. S., Gedam, Y. K., Randhave, D. S., & Deshmukh, R. R. (2014). Character Recognition of Gujarati and Devnagari Script: A Review.International Journal of Engineering, 3(1).
- Patel, C., & Desai, A. (2013). Extraction of Characters and Modifiers from Handwritten Gujarati Words. International Journal of Computer Applications,73(3).
- Patel, C., & Desai, A. (2013, June). Gujarati handwritten character recognition using hybrid method based on binary tree-classifier and k-nearest neighbour. In International Journal of Engineering Research and Technology(Vol. 2, No. 6 (June-2013)). ESRSA Publications.
- Shah, M., & Jethava, G. B. (2013). A literature review on hand written character recognition. Indian streams research journal, 3(2), 1-19.
- Indira, B., Qureshi, M. S., Shaik, M. S., Saqib, R. M., & Murthy, M. R. (2012). Devanagari Character Recognition: A Short Review. International Journal of Computer Applications, 59(6).
- Sojitra, B., & Dhakad, V. (2012). Neural Network in Character Recognition of Gujarati Script. Journal of Information, Knowledge and Research in Computer Engineering, 2(2), 269-272.
- Desai, A. A. (2012). Segmentation of characters from old typewritten documents using radon transform. Int. J. Comput. Appl, 37(9), 0975-8887.
- Maloo, M., & Kale, K. V. (2011). Gujarati script recognition: a review.International Journal of Computer Science Issues, 8(4).
- Singh, B., Mittal, A., Ansari, M. A., & Ghosh, D. (2011). Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach.International Journal on Computer Science and Engineering, 3(4), 1658-1665.
- Dongre, V. J., & Mankar, V. H. (2011). A review of research on Devnagari character recognition. arXiv preprint arXiv:1101.2491.
- Hanmandlu, M., Murthy, O. R., & Madasu, V. K. (2007, December). Fuzzy Model based recognition of handwritten Hindi characters. In Digital Image Computing Techniques and Applications, 9th Biennial Conference of the Australian Pattern Recognition Society on (pp. 454-461). IEEE.
- Kasturi, R., O’gorman, L., & Govindaraju, V. (2002). Document image analysis: A primer. Sadhana, 27(1), 3-22.
- Dileep, D. (2012). A feature extraction technique based on character geometry for character recognition. Department of Electronics and Communication Engineering, Amrita School of Engineering, Kollam, India.
- http://stackoverflow.com/questions/18620977/best-setting-for-scanners-for-scanning- documentstiff-and-pdf
- Singh, T. R., Roy, S., Singh, O. I., Sinam, T., & Singh, K. (2012). A new local adaptive thresholding technique in binarization. arXiv preprint arXiv:1201.5227.
- Gatos, B., Papamarkos, N., & Chamzas, C. (1997). Skew detection and text line position determination in digitized documents. Pattern Recognition, 30(9), 1505-1519.
- Liu, C. L., Nakashima, K., Sako, H., & Fujisawa, H. (2004). Handwritten digit recognition: investigation of normalization and feature extraction techniques. Pattern Recognition, 37(2), 265-279.
- Arica, N., & Yarman-Vural, F. T. (2001). An overview of character recognition focused on off-line handwriting. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31(2), 216-233.
- Seven grayscale conversion algorithms (with pseudocode and VB6 source code) http://www.tannerhelland.com/3643/grayscale-image-algorithm-vb6/
Index Terms
Computer Science
Information Sciences
Keywords