Manuscript Number : CSEIT217214

Recognition of Offline Hand Written Telugu Script using Deep Learning

Authors(2) :-T Madhavi Kumari, A Vinaya Babu

Automatic character recognition is one of the significant parameters that allow the data processor to distinguish letters and numbers, using contextual data. Various efforts have been made to resolve this problem using different choices of classifiers and attributes, however problem remains complex. In this work, mainly focuses on the problem of handwriting recognition (HWCR) for a script in Telugu. A crosscutting structure is used for segments an image from text, classifies characters, and selects strings using a language model is provided. Segmentation is based on mathematical morphology. Writing in Telugu is a complex alpha syllable. For this suitable language is required, which complicates the problem. Conventional methods used artisanal characteristics, which required a priori knowledge of the field, which is not always possible. In this case, automatic feature retrieval could potentially attract more interest. In this work, a classical Convolutional Neural Networks (CNN) for the identification of Telugu symbols offline is presented. Qualified analysis demonstrated the effectiveness of the proposed CNN compared to previous methods with an interesting data set. In the test data set, the classification method provided with n accuracy of 98.7%.

Authors and Affiliations

T Madhavi Kumari
Associate Professor, Dept. of ECE, JNTUH College of Engineering, Hyderabad, Telangana, India
A Vinaya Babu
Professor, Department of CSE & Dean Academics, Stanley College of Engineering, Hyderabad, Telangana, India

CNN, HWCR, Telugu, Machine Learning, Neural Networks

  1. P. Sujatha, D. Lalitha Bhaskari, Telugu and Hindi Script Recognition using Deep learning Techniques, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8 (11), Sept-2019, pp.1758-1764.
  2. Murthy Pr, Prasad C. Recognition Of Online Handwritten Telugu Letters For Different Domains And Organizations. Journal of Critical Reviews. 2019;6(6):33-40.
  3. Guha R, Das N, Kundu M, Nasipuri M, Santosh KC. DevNet: an efficient CNN architecture for handwritten Devanagari character recognition. International Journal of Pattern Recognition and Artificial Intelligence. 2020 Nov 30;34(12):2052009.
  4. Achanta R, Hastie T. Telugu OCR framework using deep learning. arXiv preprint arXiv:1509.05962. 2015 Sep 20.
  5. Angadi A, Kumari Vatsavayi V, Keerthi Gorripati S. A Deep Learning Approach to Recognize Handwritten Telugu Character Using Convolution Neural Networks. International Journal of Information Systems & Management Science. 2018 Apr 2;1(2).
  6. Reddy PC, Sureshbabu A. An applied time series forecasting model for yield prediction of agricultural crop. InInternational Conference on Soft Computing and Signal Processing 2019 Jun 21 (pp. 177-187). Springer, Singapore.
  7. Prakash KC, Srikar YM, Trishal G, Mandal S, Channappayya SS. Optical character recognition (ocr) for telugu: Database, algorithm and application. In2018 25th IEEE International Conference on Image Processing (ICIP) 2018 Oct 7 (pp. 3963-3967). IEEE.
  8. Chary BD, Sucharitha Y, shaker Reddy PC. Way to Learning Dropouts in Distance Education-A Study using Data mining Techniques.
  9. Fields G, Vaddeswaram GD. Handwritten Telugu composite character recognition using morphological analysis. International Journal of Pure and Applied Mathematics. 2018;119(18):667-76.
  10. Reddy PC, Babu AS. A Novel Approach To Analysis District Level Long Scale Seasonal Forecasting Of Monsoon Rainfall In Andhra Pradesh And Telangana. International Journal of Advanced Research in Computer Science. 2017 Nov 1;8(9).
  11. Rajashekararadhya SV, Ranjan PV. Zone-based hybrid feature extraction algorithm for handwritten numeral recognition of two popular Indian scripts. In2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) 2009 Dec 9 (pp. 526-530). IEEE.
  12. Pal U, Sharma N, Wakabayashi T, Kimura F. Handwritten numeral recognition of six popular Indian scripts. InNinth International Conference on Document Analysis and Recognition (ICDAR 2007) 2007 Sep 23 (Vol. 2, pp. 749-753). IEEE.
  13. Shrivastava SK, Gharde SS. Support vector machine for handwritten Devanagari numeral recognition. International journal of computer applications. 2010 Oct;7(11):9-14.
  14. Sastry PN, Lakshmi TV, Rao NK, Rajinikanth TV, Wahab A. Telugu handwritten character recognition using zoning features. In2014 International Conference on IT Convergence and Security (ICITCS) 2014 Oct 28 (pp. 1-4). IEEE.
  15. Rao, K. Vijay Kumar R. Rajeshwara. Improvement in Efficiency of Recognition of Handwritten Telugu Script. International Journal of Inventive Engineering and Sciences (IJIES) Volume-2, Issue-1, December 2013.
  16. Dara R, Panduga U. Telugu handwritten isolated characters recognition using two dimensional fast fourier transform and support vector machine. International Journal of Computer Applications. 2015 Jan 1;116(5).
  17. N. Das, J. M. Reddy, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri and D. K. Basu, A statistical-topological feature combination for recognition of handwritten numerals, Applied Soft Computing Journal 12(8) (2012) 2486–2495.
  18. M. Hanmandlu and O. V. R. Murthy, Fuzzy model based recognition of handwritten numerals, Pattern Recognition 40(6) (2007) 1840–1854.
  19. Patil PM, Sontakke TR. Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network. Pattern Recognition. 2007 Jul 1;40(7):2110-7.
  20. A. Kumar Pant, P. Kumar Gyawali and S. Acharya, Deep Learning Based Large Scale Handwritten Devanagari Character Recognition.
  21. D. S. Maitra, U. Bhattacharya and S. K. Parui, CNN based common approach to handwritten character recognition of multiple scripts, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 2015-Novem (2015) 1021–1025.
  22. Sarkhel R, Das N, Das A, Kundu M, Nasipuri M. A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts. Pattern Recognition. 2017 Nov 1;71:78-93.
  23. Shaker Reddy PC, Sureshbabu A. An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction. International Journal of Sensors Wireless Communications and Control. 2020 Aug 1;10(4):473-83.

Publication Details

Published in : Volume 7 | Issue 2 | March-April 2021
Date of Publication : 2021-03-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 661-671
Manuscript Number : CSEIT217214
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

T Madhavi Kumari, A Vinaya Babu, "Recognition of Offline Hand Written Telugu Script using Deep Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 2, pp.661-671, March-April-2021. |          | BibTeX | RIS | CSV

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