RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING
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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.
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References
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
Bati, E. (2014). Deep convolutional neural networks with an application towards geospatial object Recognition. Diss. Middle East Technical University Ankara.
Elitez, E. (2015). Handwritten digit string segmentation and recognition using deep learning. Diss. Middle East Technical University Ankara.
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
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.
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.
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.
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.
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.
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
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
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
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
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
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
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