Optical Character Recognition Using Deep Learning and OpenCV Techniques
DOI:
https://doi.org/10.32628/CSEIT195386Keywords:
Character Segmentation, Convolutional Neural Network, Long Short-Term Memory Networks, Classification.Abstract
The problem of image to text-based conversion is persisting in many areas of applications. This project seeks to classify an individual handwritten character so that handwritten text can be translated to a digital form. We used two main approaches to accomplish this task: classifying digits directly and character segmentation. For the former, we use Convolutional Neural Network (CNN) with various architectures to train a model that can accurately classify characters. For the latter, we use Long Short-Term Memory networks (LSTM) with convolution to construct bounding boxes for each character.
References
- Alex Graves, Santiago Fernandez, Faustino Gomez, Juergen Schmidhuber, Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks, Proceedings of the 23rd international conference on Machine learning. 2006
- Convolutional Neural Network Benchmarks: https://github.com/jcjohnson/cnn-benchmarks
- Elie Krevat, Elliot Cuzzillo. Improving Off-line Handwritten Character Recognition with Hidden Markov Models.
- Fabian Tschopp. Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems George Nagy. Document processing applications.
- Mail encoding and processing system patent: https://www.google.com/patents/US5420403
- Kurzweil Computer Products. http://www.kurzweiltech.com/kcp.html
- H. Bunke1, M. Roth1, E.G. Schukat-Talamazzini. Offline Cursive Handwriting Recognition using Hidden Markov Models.
- K. Simonyan, A. Zisserman Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv technical report, 2014
- Lisa Yan. Recognizing Handwritten Characters. CS 231N Final Research Paper.
- Oivind Due Trier, Anil K. Jain, Torfinn Text. Feature Extraction Methods for Character Recognition–A Survey. Pattern Recognition. 1996
- Satti, D.A., 2013, Offline Urdu Nastaliq OCR for Printed Text using Analytical Approach. MS thesis report Quaid-i-Azam University: Islamabad, Pakistan. p. 141.
- Mahmoud, S.A., & Al-Badr, B., 1995, Survey and bibliography of Arabic optical text recognition. Signal processing, 41(1), 49-77.
- Bhansali, M., & Kumar, P, 2013, An Alternative Method for Facilitating Cheque Clearance Using Smart Phones Application. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(1), 211-217.
- Qadri, M.T., & Asif, M, 2009, Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition presented at International Conference on Education Technology and Computer, Singapore, 2009. Singapore: IEEE.
- Shen, H., & Coughlan, J.M, 2012, Towards A Real Time System for Finding and Reading Signs for Visually Impaired Users. Computers Helping People with Special Needs. Linz, Austria: Springer International Publishing.
- Bhavani, S., & Thanushkodi, K, 2010, A Survey On Coding Algorithms In Medical Image Compression. International Journal on Computer Science and Engineering, 2(5), 1429-1434.
- Bhammar, M.B., & Mehta, K.A, 2012, Survey of various image compression techniques. International Journal on Darshan Institute of Engineering Research & Emerging Technologies, 1(1), 85-90.
Downloads
Published
2019-07-30
Issue
Section
Research Articles
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
[1]
Shallu Juneja, Dipti Nayan, Rishabh Bhardwaj
, "
Optical Character Recognition Using Deep Learning and OpenCV Techniques" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307,
Volume 5, Issue 4, pp.05-09, July-August-2019. Available at doi : https://doi.org/10.32628/CSEIT195386