Application of Deep Convolutional Neural Networks to Telugu Scriptsfor Optical Character Recognition

© 2023 by IJCTT Journal
Volume-71 Issue-1
Year of Publication : 2023
Authors : M. Gnaneswari, T. Chaitanya Kumar
DOI :  10.14445/22312803/IJCTT-V71I1P108

How to Cite?

M. Gnaneswari, T. Chaitanya Kumar, "Application of Deep Convolutional Neural Networks to Telugu Scriptsfor Optical Character Recognition," International Journal of Computer Trends and Technology, vol. 71, no. 1, pp. 50-55, 2023. Crossref,

This research looks at the process of optical character recognition (OCR) for Telugu scripts. Telugu is anIndian Dravidian language. In English, optical character recognition is widely used, and there is a plethora of smartphone apps available. Telugu has a significantly higher level of complexity due to the number of output classesthat can be formed and the inter-class diversity. In addition, there are no good Telugu OCR systems. We employedthe Deep Convolutional Neural Network (DCNN) model for Telugu character recognition because of its success in other domains, such as segmentation, object identification, and character recognition. Multiple machine learning algorithms such as AdaBoost, Support Vector Machine (SVM), XGBoost, and Decision Tree (DT) are considered for performance evaluation of the proposed DCNN. The proposed DCNN for recognizing the Telugu scripts has yielded promising results, demonstrating its usefulness when compared to other traditional techniques when experimented with using the IEEE Telugu Character Dataset.

Text Recognition, Telugu Script, Optical Character Recognition (OCR), Deep Learning, Convolutional Neural Network (CNN), Machine Learning.


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