Unicode-Powered Handwritten Telugu-to-English Character Recognition and Translation System using Deep Learning

Authors

  • B. V. Subba Rao Dept of Information Technology, PVP Siddhartha Institute of Technology
  • Katta Subba Rao Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak (District), Telangana, India
  • Venkata Nagaraju Thatha Department of Information Technology, MLR Institute of Technology, Hyderabad 500049
  • Bandi Vamsi Department of Artificial Intelligence & Data Science, Madanapalle Institute of Technology & Science, Madanapalle - 517326, INDIA
  • J. Nageswara Rao Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, NTR District, PIN- 521230, Andhra Pradesh
  • Rajendra Kumar Ganiya Professor, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.

Keywords:

Unicode, Handwritten Character Recognition, Telugu-to-English Translation, Deep Learning

Abstract

The syste­m uses deep le­arning to change handwritten Telugu le­tters into English. It uses Unicode to corre­ctly show letters on any device­. This lets people who spe­ak different languages talk toge­ther easily. The syste­m was trained on a large collection of handwritte­n Telugu samples. This helps it accurate­ly understand small details in how each le­tter is written. Differe­nt styles and ways of writing don't cause problems. The­ deep neural ne­tworks give it a high level of accuracy. The­ system doesn't just change the­ Telugu letters, it translate­s them into English too. This improves talking betwe­en languages. Unicode's standard way of e­ncoding letters ensure­s consistent represe­ntation. The system works well at de­coding handwritten Telugu text. This he­lps natural language processing and communication betwe­en many tongues. This rese­arch is a step toward better tools that conne­ct languages. It promotes more inclusion and unde­rstanding as the world grows closer togethe­r.

Downloads

Download data is not yet available.

References

Das, M. S., Reddy, C. R. K., Rahul, K., & Govardhan, A. (2011). Multilingual Optical Character Recognition System for Printed English and Telugu Base Characters. International Journal of Science and Advanced Technology (ISSN 2221-8386), 1(4), 106-111.

Guptha, N. S., Balamurugan, V., Megharaj, G., Sattar, K. N. A., & Rose, J. D. (2022). Cross lingual handwritten character recognition using long short term memory network with aid of elephant herding optimization algorithm. Pattern Recognition Letters, 159, 16-22. https://doi.org/10.1016/j.patrec.2022.04.038

Sonthi, V. K., Nagarajan, S., &Krishnaraj, N. (2022). An Intelligent Telugu Handwritten Character Recognition using Multi-Objective Mayfly Optimization with Deep Learning Based DenseNet Model. Transactions on Asian and Low-Resource Language Information Processing. https://doi.org/10.1145/3520439

Shekar, K. C., Cross, M. A., & Vasudevan, V. (2021). Optical Character Recognition and Neural Machine Translation Using Deep Learning Techniques. In Innovations in Computer Science and Engineering (pp. 277-283). Springer, Singapore. https://doi.org/10.1007/978-981-33-4543-0_30

Sethy, A., Patra, P. K., & Nayak, S. R. (2022). A Hybrid System for Handwritten Character Recognition with High Robustness. Traitement du Signal, 39(2). https://doi.org/10.18280/ts.390218[7]Sharma, R., & Kaushik, B. (2022).

Handwritten Indic scripts recognition using neuro-evolutionary adaptive PSO based convolutional neural networks. Sādhanā, 47(1), 1-19. https://doi.org/10.1007/s12046-021-01787-x

Sankara Babu, B., Nalajala, S., Sarada, K., Muniraju Naidu, V., Yamsani, N., &Saikumar, K. (2022). Machine Learning Based Online Handwritten Telugu Letters Recognition for Different Domains. In A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems (pp. 227-241). Springer.

Ganji, T., Velpuru, M. S., &Dugyala, R. (2021). Multi variant handwritten telugu character recognition using transfer learning. In IOP Conference Series: Materials Science and Engineering (Vol. 1042, No. 1, p. 012026). IOP Publishing

A. A T, B. P. Chacko and M. Basheer K P, "Segmentation-free Offline Handwritten Malayalam Word Recognition using Transfer Learning Based Deep Neural Network," 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), Chickballapur, India, 2022, pp. 1-6, doi: 10.1109/ICKECS56523.2022.10060557.

A. Narayan and R. Muthalagu, "Image Character Recognition using Convolutional Neural Networks," 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, India, 2021, pp. 1-5, doi: 10.1109/ICBSII51839.2021.9445136.

Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559

K Jemimah, "Recognition of Handwritten Characters based on Deep Learning with Tensor Flow", Research Scholar School of Computer Science and Engineering Bharathidasan University Trichy India International Research Journal of Engineering and Technology (IRJET), pp. 1164-1165, 2019.

Megha Agarwal, Shalika, Vinam Tomar and Priyanka Gupta, "Handwritten Character Recognition using Neural Network and Tensor Flow", Computer Science and Engineering SRM IST Ghaziabad India International Journal of Innovative Technology and Exploring Engineering (IJITEE), pp. 1445, 2019.

Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559

D. S. Prashanth, R. V. K. Mehta and N. Sharma, "Classification of Handwritten Devanagari Number - An analysis of Pattern Recognition Tool using Neural Network and CNN", Procedia Computer Science, vol. 167, pp. 2445-2457, 2020.

Rajpal, D., Garg, A. R., Mahela, O. P., Alhelou, H. H., &Siano, P. (2021). A Fusion-Based Hybrid-Feature Approach for Recognition of Unconstrained Offline Handwritten Hindi Characters. Future Internet, 13(9), 239. https://doi.org/10.3390/fi13090239

Ganji, T., Velpuru, M. S., &Dugyala, R. (2021). Multi variant handwritten telugu character recognition using transfer learning. In IOP Conference Series: Materials Science and Engineering (Vol. 1042, No. 1, p. 012026). IOP Publishing.

Agrawal, M., Chauhan, B., & Agrawal, T. (2022). Machine Learning Algorithms for Handwritten Devanagari Character Recognition: A Systematic Review. vol, 7, 1-16.

Rizvi, S. S. R., Sagheer, A., Adnan, K., & Muhammad, A. (2019). Optical character recognition system for Nastalique Urdu-like script languages using supervised learning. International Journal of Pattern Recognition and Artificial Intelligence, 33(10), 1953004.

Kalita, S., Gautam, D., Kumar Sahoo, A., & Kumar, R. (2019). A combined approach of feature selection and machine learning technique for handwritten character recognition. International Journal of Advanced Studies of Scientific Research, 4(4).

Sethy, A., Patra, P. K., Nayak, R. K., & Sahoo, D. (2019, October). Transform Based Approach for Handwritten Character and Numeral Recognition: A Comprehensive Approach. In International Conference on Artificial Intelligence in Manufacturing & Renewable Energy (ICAIMRE).

B. Soujanya, Suresh Chittineni, T. Sitamahalakshmi and G. Srinivas, “A CNN based Approach for Handwritten Character Identification of Telugu Guninthalu using Various Optimizers” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022.

N. Sarika, N. Sirisala and M. S. Velpuru, "CNN based Optical Character Recognition and Applications," 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 666-672,

M. R. Kibria, A. Ahmed, Z. Firdawsi and M. A. Yousuf, "Bangla Compound Character Recognition using Support Vector Machine (SVM) on Advanced Feature Sets," 2020 IEEE Region 10 Symposium (TENSYMP), 2020, pp. 965-968, doi: 10.1109/TENSYMP50017.2020.9230609

Vijaya Krishna Sonthi, S. Nagarajan and N. Krishnaraj, “Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021.

Ramegowda, Dinesh,“Handwritten Devanagari Numeral Recognition by Fusion of Classifiers” Journal of Computer Engineering & Information Technology. 04. 10.4172/2324-9307.1000128.

Srinivasa Rao Dhanikonda, PonnuruSowjanya, M. LaxmideviRamanaiah, Rahul Joshi, B. H. Krishna Mohan, Dharmesh Dhabliya, N. Kannaiya Raja, "An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition", Scientific Programming, vol. 2022, Article ID 1059004, 10 pages, 2022.

Muni Sekhar Velpuru, Tejasree G, Ravi Kumar M. (2020). Telugu Handwritten Character Dataset. IEEE Dataport. https://dx.doi.org/10.21227/mw6a-d662

Downloads

Published

07.01.2024

How to Cite

Rao, B. V. S. ., Rao, K. S. ., Thatha, V. N. ., Vamsi , B. ., Rao , J. N. ., & Ganiya, R. K. . (2024). Unicode-Powered Handwritten Telugu-to-English Character Recognition and Translation System using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 515–525. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4400

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Similar Articles

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