RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING
Manikandan SRIDHARAN
profmaninvp@gmail.comDepartment 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, FilteringReferences
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
Authors
Manikandan SRIDHARANprofmaninvp@gmail.com
Department of Information Technology, E. G. S. Pillay Engineering College, Nagapattinam, Tamil Nadu India
Authors
Delphin Carolina RANI ARULANANDAMDepartment of Computer Science and Engineering, K. Ramakrishnan College of Technology, Samayapuram, Tiruchirappalli, Tamil Nadu India
Authors
Rajeswari K CHINNASAMYDepartment of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu India
Authors
Suma THIMMANNASri Venkateshwara College of Engineering, Bengaluru, Karnataka India
Authors
Sivabalaselvamani DHANDAPANIDepartment of Computer Applications, Kongu Engineering College, Perundurai, Erode, Tamil Nadu India
Statistics
Abstract views: 194PDF downloads: 63
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
- Kusay F. AL-TABATABAIE, Sadeer D. ABDULAMEER, APPLYING ARDUINO FOR CONTROLLING CAR PARKING SYSTEM , Applied Computer Science: Vol. 15 No. 2 (2019)
- Jarelh Galdos, Nikolai Lopez, Angie Medina, Jorge Huarca, Jorge Rendulich, Erasmo Sulla, COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES , Applied Computer Science: Vol. 20 No. 1 (2024)
- Esraa Alaa MAHAREEK, Doaa Rizk FATHY, Eman Karm ELSAYED, Nahed ELDESOUKY, Kamal Abdelraouf ELDAHSHAN, VIOLENCE PREDICTION IN SURVEILLANCE VIDEOS , Applied Computer Science: Vol. 20 No. 3 (2024)
- Ghania Zidani, Djalal DJARAH, Abdslam BENMAKHLOUF, Laid KHETTACHE, OPTIMIZING PEDESTRIAN TRACKING FOR ROBUST PERCEPTION WITH YOLOv8 AND DEEPSORT , Applied Computer Science: Vol. 20 No. 1 (2024)
- Mahmoud BAKR, Sayed ABDEL-GABER, Mona NASR, Maryam HAZMAN, TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Hanan M. SHUKUR, Shavan ASKAR, Subhi R.M. ZEEBAREE, THE UTILIZATION OF 6G IN INDUSTRY 4.0 , Applied Computer Science: Vol. 20 No. 2 (2024)
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
- Saleh ALBAHLI, A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Lei Liu, Eric B. Blancaflor, Mideth Abisado, A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO , Applied Computer Science: Vol. 19 No. 1 (2023)
- Raphael Olufemi AKINYEDE, Temitayo Elijah BALOGUN, Abiodun Boluwade ROTIMI, Oluwasefunmi Busola FAMODIMU, A CUSTOMER-CENTRIC APPLICATION FOR A CINEMA HOUSE , Applied Computer Science: Vol. 16 No. 2 (2020)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
