cs 18: e4

Research Article

Intelligent Character Recognition System Using Convolutional Neural Network

Download8 downloads
  • @ARTICLE{10.4108/eai.16-10-2020.166659,
        author={S. Suriya and Dhivya S and Balaji M},
        title={Intelligent Character Recognition System Using Convolutional Neural Network},
        journal={EAI Endorsed Transactions on Cloud Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CS},
        year={2020},
        month={10},
        keywords={Computational Linguistics, Character Recognition, Convolutional Neural Networks, Machine Learning},
        doi={10.4108/eai.16-10-2020.166659}
    }
    
  • S. Suriya
    Dhivya S
    Balaji M
    Year: 2020
    Intelligent Character Recognition System Using Convolutional Neural Network
    CS
    EAI
    DOI: 10.4108/eai.16-10-2020.166659
S. Suriya1,*, Dhivya S2, Balaji M3
  • 1: Associate Professor, Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, India
  • 2: PG Scholar, Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, India
  • 3: Software Engineer, Arcesium
*Contact email: suriyas84@gmail.com

Abstract

Computational Linguistics involves the techniques of Computer Science which play a vital role in recognizing written or printed characters such as numbers or letters to change them into a form that the computer can use it efficiently. Convolutional Neural Network differs from other approaches by extracting the features automatically. The proposed approach is capable of recognizing characters in a variety of challenging conditions using the Convolutional Neural Network, where traditional character recognition systems fail, notably in the presence of low resolution, substantial blur, low contrast, and other distortions. Intellectual Character Recognition System is an application that uses Convolutional Neural Network (CNN) to recognize the Tamil character dataset accurately developed by HP Labs India. The novelty of this system is that, it recognizes the characters of the Predominant Tamil language. With the help of suitable datasets consisting of the Tamil Scripts, the model is trained efficiently. This work has produced a training accuracy of 99.16% which is far better compared to the traditional approaches.