RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING

Manikandan SRIDHARAN

profmaninvp@gmail.com
Department of Information Technology, E. G. S. Pillay Engineering College, Nagapattinam, Tamil Nadu (India)

Delphin Carolina RANI ARULANANDAM


Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Samayapuram, Tiruchirappalli, Tamil Nadu (India)

Rajeswari K CHINNASAMY


Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu (India)

Suma THIMMANNA


Sri Venkateshwara College of Engineering, Bengaluru, Karnataka (India)

Sivabalaselvamani DHANDAPANI


Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, Tamil Nadu (India)

Abstract

This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.


Keywords:

Deep Convolution Network, Tamil Letter, Recognition System, Font Recognition, Filtering

Adomavicius, G., & Tuzhilin, A. (2018). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. http://dx.doi.org/10.1109/TKDE.2005.99
DOI: https://doi.org/10.1109/TKDE.2005.99   Google Scholar

Bati, E. (2014). Deep convolutional neural networks with an application towards geospatial object Recognition. Diss. Middle East Technical University Ankara.
  Google Scholar

Elitez, E. (2015). Handwritten digit string segmentation and recognition using deep learning. Diss. Middle East Technical University Ankara.
  Google Scholar

Jaiem, F.K., Slimane, F., & Kherallah, M. (2017). Arabic font recognition system applied to different text entity level analysis. 2017 International Conference on Smart, Monitored and Controlled Cities (SM2C), 36–40. http://dx.doi.org/10.1109/SM2C.2017.8071847
DOI: https://doi.org/10.1109/SM2C.2017.8071847   Google Scholar

Koyun, A., & Afsin, E. (2017). 2D optical character recognition based on deep learning. Journal of Turkey Informatics Foundation of Computer Science and Engineering, 10(1), 11–14.
  Google Scholar

Manikandan, S., & Chinnadurai, M. (2019). Intelligent and Deep Learning Approach OT Measure E- Learning Content in Online Distance Education. The Online Journal of Distance Education and e-Learning, 7(3), 199–204.
  Google Scholar

Manikandan, S., & Chinnadurai, M. (2020). Evaluation of Students’ Performance in Educational Sciences and Prediction of Future Development using TensorFlow. International Journal of Engineering Education, 36(6), 1783–1790.
  Google Scholar

Manikandan, S., Chinnadurai, M., Maria Manuel Vianny, D., & Sivabalaselvamani, D. (2020). Real Time Traffic Flow Prediction and Intelligent Traffic Control from Remote Location for Large-Scale Heterogeneous Networking using TensorFlow. International Journal of Future Generation Communication and Networking, 13(1), 1006–1012.
  Google Scholar

Manikandan, S., Dhanalakshmi, P., Priya, S., & Mary OdilyaTeena, A. (2021). Intelligent and Deep Learning Collaborative method for E-Learning Educational Platform using TensorFlow. Turkish Journal of Computer and Mathematics Education, 12(10), 2669–2676.
  Google Scholar

Sathiyamoorthi, V. (2016). A novel cache replacement policy for Web proxy caching system using Web usage mining. International Journal of Information Technology and Web Engineering, 11(2), 1–13. http://dx.doi.org/10.4018/IJITWE.2016040101
DOI: https://doi.org/10.4018/IJITWE.2016040101   Google Scholar

Sevik, A., Erdogmus, P., & Yalein, E. (2018). Font and Turkish Letter Recognition in Images with Deep Learning. International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (pp. 61–64). IEEE. http://dx.doi.org/10.1109/IBIGDELFT.2018.8625333
DOI: https://doi.org/10.1109/IBIGDELFT.2018.8625333   Google Scholar

Shanthi ,T., & Sabeenian, R.S. (2019). Modified Alexnet architecture for classification of diabetic retinopathy images. Computers and Electrical Engineering, 76, 56–64. http://dx.doi.org/10.1016/j.compeleceng.2019.03.004
DOI: https://doi.org/10.1016/j.compeleceng.2019.03.004   Google Scholar

Tajmir, S.H., & Alkasab, T.K. (2018). Toward augmented radiologists: changes in radiology education in the era of machine learning and artificial intelligence. Academic radiology, 25(6), 747–750. https://doi.org/10.1016/j.acra.2018.03.007
DOI: https://doi.org/10.1016/j.acra.2018.03.007   Google Scholar

Yuan, Y., Mou, L., & Lu, X. (2015). Scene recognition by manifold regularized deep learning architecture. In IEEE Transactions on Neural Networks and Learning Systems, (vol. 26(10), pp. 2222–2233). IEEE. http://dx.doi.org/10.1109/TNNLS.2014.2359471
DOI: https://doi.org/10.1109/TNNLS.2014.2359471   Google Scholar

Zhou, Y., & Tuzel, O. (2017). Voxelnet: End-to-end learning for point cloud based 3d object detection. arXiv:1711.06396. https://arxiv.org/abs/1711.06396
DOI: https://doi.org/10.1109/CVPR.2018.00472   Google Scholar

Download


Published
2021-06-30

Cited by

SRIDHARAN, M., RANI ARULANANDAM, D. C. ., CHINNASAMY, R. K., THIMMANNA, S. ., & DHANDAPANI, S. (2021). RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING. Applied Computer Science, 17(2), 90–99. https://doi.org/10.23743/acs-2021-15

Authors

Manikandan SRIDHARAN 
profmaninvp@gmail.com
Department of Information Technology, E. G. S. Pillay Engineering College, Nagapattinam, Tamil Nadu India

Authors

Delphin Carolina RANI ARULANANDAM 

Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Samayapuram, Tiruchirappalli, Tamil Nadu India

Authors

Rajeswari K CHINNASAMY 

Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu India

Authors

Suma THIMMANNA 

Sri Venkateshwara College of Engineering, Bengaluru, Karnataka India

Authors

Sivabalaselvamani DHANDAPANI 

Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, Tamil Nadu India

Statistics

Abstract views: 194
PDF downloads: 63


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.


Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.