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Handwriting Recognition System Using Optical Character Recognition
Priti Gangania1 , Sowmya Mishra2 , Shreshtha Garg3 , Sonam Agarwal4
Section:Research Paper, Product Type: Isroset-Journal
Vol.6 ,
Issue.3 , pp.18-21, Jun-2018
CrossRef-DOI: https://doi.org/10.26438/ijsrcse/v6i3.1821
Online published on Jun 30, 2018
Copyright © Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal, “Handwriting Recognition System Using Optical Character Recognition,” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.18-21, 2018.
MLA Style Citation: Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal "Handwriting Recognition System Using Optical Character Recognition." International Journal of Scientific Research in Computer Science and Engineering 6.3 (2018): 18-21.
APA Style Citation: Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal, (2018). Handwriting Recognition System Using Optical Character Recognition. International Journal of Scientific Research in Computer Science and Engineering, 6(3), 18-21.
BibTex Style Citation:
@article{Gangania_2018,
author = {Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal},
title = {Handwriting Recognition System Using Optical Character Recognition},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {6 2018},
volume = {6},
Issue = {3},
month = {6},
year = {2018},
issn = {2347-2693},
pages = {18-21},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=642},
doi = {https://doi.org/10.26438/ijcse/v6i3.1821}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i3.1821}
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=642
TI - Handwriting Recognition System Using Optical Character Recognition
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal
PY - 2018
DA - 2018/06/30
PB - IJCSE, Indore, INDIA
SP - 18-21
IS - 3
VL - 6
SN - 2347-2693
ER -
Abstract :
This is an overview of the most recent published approaches to solving the handwriting recognition problem. This paper is aimed at clarifying the role of handwriting recognition in accordance with today`s maturing technologies. It tries to list and clarify the components that build handwriting recognition and related technologies such as OCR (Optical Character Recognition) and Signature Verification. This paper could also be regarded as a survey of handwriting recognition and related topics with a rich list of references for the interested reader. A level of practicality of use of this technology for different languages and cultures is also discussed.
Key-Words / Index Term :
OCR, Language Model
References :
[1]. Bhatia Neetu, “Optical Character Recognition Techniques”, International Conference of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014.
[2]. Goutam Sarker, Monica Besra, Silpi Dhua. (2015) A Malsburg learning back propagation combination for handwritten alpha numeral recognition. 2015 International Conference on Advances in Computer Engineering and Applications, 493-498
[3]. Fahim Irfan Alam, Bithi Banik. (2013) Offline isolated bangla handwritten character recognition using spatial relationships. 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 1-6.
[4]. Amjad Rehman, Tanzila Saba. (2012) Off-line cursive script recognition: current advances, comparisons and remaining problems. Artificial Intelligence Review.
[5]. A.K.Gupta, S.Gupta Research Paper | Isroset-Journal (IJSRCSE) Vol.6 , Issue.2 , pp.38-40, Apr-2018, CrossRef-DOI:https://doi.org/10.26438/ijsrcse/v6i2.3840, Neural Network through face recognition.
[6]. Haralick R. M.; Shanmugam K.; Dinstein I. (1973): Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp. 610–621.
[7]. Clausi D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization, Canadian Journal of Remote Sensing, 28(1), pp. 45-62.
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