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Optical handwritten character recognition for Tamil language using CNN-VGG-16 model with RF classifier

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Abstract

In this world of modern data, it is so difficult to recognize handwritten characters for Tamil as many people have different styles of writing, so some of the letters are very difficult to understand and only a few can understand them. So, to overcome this issue, we built an algorithm in which the system could recognize the character and return the output. As it is difficult to understand letters manually for all their text, there is a need for some automatic method. The only intention of character recognition is that it wants to create a high-quality, accurate result that has the important points while considering the outlined input source image. Mostly, natural language processing and machine learning face the same problem with text recognition. The main goal of automatic character recognition is to create a high degree of accuracy as best as a human can do. Character recognition is the process of filtering the required information from the input-trained source to output the most useful content. This paper proposes a CNN-VGG16-RF model (convolution neural network-VGGNet-random forest) which employs an effective method to pick out the correct output. Experimental tests for our model were carried out to evaluate text quality, and the Tamil language dataset from the HP Tamil Lab website was used to compare our model to some other models; our model was found to be more effective in solving the handwritten recognition problem. In this model, we are going to propose Tamil vowels such as 12 letters only for the training and testing process.

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Authors and Affiliations

  1. Department of CSE, Panimalar Engineering College, Chennai, Tamilnadu, India

    N. Pughazendi & M. HariKrishnan

  2. Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu, India

    Rashmita Khilar

  3. Department of IT, Agni College of Technology, Chennai, Tamilnadu, India

    L. Sharmila

Authors
  1. N. Pughazendi
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  2. M. HariKrishnan
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  3. Rashmita Khilar
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  4. L. Sharmila
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Contributions

HM contributed on Conceptualization of proposed system and implementation. PN contributed on Mathematical Conceptualization and overall supervision. RK contributed on the Data Collection. SL contributed on the review of existing works.

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Correspondence to N. Pughazendi.

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Pughazendi, N., HariKrishnan, M., Khilar, R. et al. Optical handwritten character recognition for Tamil language using CNN-VGG-16 model with RF classifier. Opt Quant Electron 55, 976 (2023). https://doi.org/10.1007/s11082-023-05211-y

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