CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Handwritten Character Recognition using Neural Networks forBanking Applications

by Shreya Mhalgi, Ketki Ganu, Prajakta Marne, Radhika Phadke, Swati Shekapure
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 27
Year of Publication: 2020
Authors: Shreya Mhalgi, Ketki Ganu, Prajakta Marne, Radhika Phadke, Swati Shekapure
10.5120/ijca2020920296

Shreya Mhalgi, Ketki Ganu, Prajakta Marne, Radhika Phadke, Swati Shekapure . Handwritten Character Recognition using Neural Networks forBanking Applications. International Journal of Computer Applications. 176, 27 ( Jun 2020), 1-7. DOI=10.5120/ijca2020920296

@article{ 10.5120/ijca2020920296,
author = { Shreya Mhalgi, Ketki Ganu, Prajakta Marne, Radhika Phadke, Swati Shekapure },
title = { Handwritten Character Recognition using Neural Networks forBanking Applications },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 27 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number27/31365-2020920296/ },
doi = { 10.5120/ijca2020920296 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:33.574263+05:30
%A Shreya Mhalgi
%A Ketki Ganu
%A Prajakta Marne
%A Radhika Phadke
%A Swati Shekapure
%T Handwritten Character Recognition using Neural Networks forBanking Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 27
%P 1-7
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Banks often accept handwritten forms for various purposes like application for creating or closure of accounts, loans, net banking, etc. The form takes a lot of user information consisting of sensitive data viz. Aadhar card number, pan card number. This information is usually taken in pen-paper format and needs entry to the bank database to document the particulars in the system or the bank requires to store a physical copy of the form for future reference. Manual entry of these details into the bank database is a tedious process and might be erroneous at times. Also, maintaining the original copy of the form or like document generate stockpiles of paper. In an attempt to overcome these discrepancies, the proposed problem statement provides a solution by making use of Handwritten Character Recognition which will input data in the form of an image to store and maintain it in a digital library.

References
  1. Xin Jia. Image recognition method based on deep learning. 28-30 May 2017.
  2. Palak Patel Chirag I Patel, Ripal Patel. Handwritten character recognition using neural networks. International Journal of Scientific and Engineering Research, 2(5):1–6, May 2011.
  3. Ryan Nash Keiron O’Shea. An introduction to convolutional neural networks. CoRR, abs/1511.08458, 2015.
  4. Herbert F. Schantz. The history of ocr optical character recognition, 1982.
  5. Alexander Del Toro Barba(Machine Learning Specialist( Lead) at Google). How artificial intelligence is revolutionizing finance, January 2017. linkedin.com.
  6. Eugenio Culurciello. The fall of lstm/rnn, April 2018. Towards Data Science.
  7. Vladlen Koltun Shaojie Bai, J. Zico Kolter. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR, abs/1803.01271, 2018.
  8. Geoffrey So. Should we abandon lstm for cnn, March 2019 2019. Published at medium.com.
  9. Yann LeCun Xiang Zhang, Junbo Jake Zhao. Characterlevel convolutional networks for text classification. CoRR, abs/1509.01626, 2015.
  10. Shunji Mori. Historical review of ocr research and development. July 1992. Invited Paper,PROCEEDINGS OF THE IEEE. VOL 80. NO 7.
  11. S S Kumar Deepa Berchmans. Optical character recognotion: An overview and an insight. 2014. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).
  12. Ishiqaki K Akiyama K Nakagawa M Tanaka H, Nakajima K. Hybrid pen-input character recognition system based on integration of online-offline recognition. pages 209–212. Proceedings of the fifth International Conference on Document Analysis and Recognition (ICDAR-1999) .
  13. Ruwei Dai Chunmei Liu, Chunheng Wang. Low resolution character recognition by image quality evaluation. pages 864– 867. Proceedings of the Eighteenth International Conference on Pattern Recognition (ICPR 2006).
  14. Suresh RM Kannan R J, Prabhakar R. Off-line cursive handwritten tamil character recognition. pages 159–164, 2008. International Conference on Security technology.
  15. Prasad R S Prasad J R, Kulkarni U V. Offline handwritten character recognition of gujarati script using pattern matching. pages 611–614, 2009. 3 Rd International Conference on Anti-counterfeiting Security and Identification in communication.
  16. Luca Maria Gambardella Jurgen Schmidhuber Dan Claudiu Ciersan, Ueli Meier. Convolutional neural network committees for handwritten character classification. 2011. 2011 International Conference on Document Analysis and Recognition, 10.1109/ICDAR.2011.229.
  17. Yoshua Bengio Yann LeCun, Leon Bottou and Patrick Haner. Gradient-based learning applied to document recognition. pages 611–614, 1998. PROC. OF THE IEEE, NOVEMBER 1998.
  18. Tian Ruixia Yang Gang Wang Yutao, Qin Tingting. Recognition of license plate character based on wavelet transform and generalized regression neural network. pages 1881–1885, 2012. Control and Decision Conference (CCDC), 2012 24th Chinese.
  19. Alejandro Baldominos, Yago Saez, Pedro Isasi. A survey of handwritten character recognition with mnist and emnist. MDPI, Appl. Sci.2019, (15), 3169.
  20. Rachel Wiles. Have we solved the problem of handwriting recognition?, June 2019. towardsdatasicence.com.
  21. Swarnendu Ghosh Ritesh Sarkhel Bodhisatwa Mandal, Suvam Dubey and Nibaran Das. Handwritten indic character recognition using capsule networks. CoRR, abs/1901.00166, 2019.
  22. Hirunima Jayasekara Jathushan Rajasegaran Suranga Seneviratne Vinoj Jayasundara, Sandaru Jayasekara and Ranga Rodrigo. Handwritten character recognition with very small datasets. CoRR, abs/1904.08095, 2019.
  23. Raymond Kurzweil. Introduces the first omni-font optical character recognition system, 1974. HistoryofInformation. com.
  24. Nist handprinted forms and characters database. nist.gov.
  25. Sourabh Sinha. Python — gray scaling of images using opencv. GeeksforGeeks-Acomputer science portal for geeks.
  26. Types of morphological operations. mathworks.com.
  27. Image processing in idl. northstar-dartmouth.edu.
  28. Geoffrey E. Hinton Alex krizhevsky, Ilya Sutskever. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 (NIPS), 2012.
  29. Andrew Zisserman Karen Simonyan. Very deep convolutional networks for large-scale image classification. 2015. ICLR conference.
  30. Tensorflow. https://www.tensorflow.org/.
  31. Why use keras. Keras documentation. keras.io.
  32. Miscellaneous operating system interfaces. Documentation the Python standard library.
  33. Chinmoy Lenka. File handling in python. GeeksforGeeks-A computer science portal for geeks.
  34. Tutorial, pyfpdf. https://pyfpdf.readthedocs.io/en/latest/Tutorial/index.html
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Deep learning Convolutional Neural Networks(CNN) Handwritten Character Recognition(HCR)

Powered by PhDFocusTM