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Artificial neural network based character recognition using SciLab

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Abstract

Character recognition (CR) from an image of text is challenging research in pattern recognition and image processing. New CR systems use artificial neural network (ANN) methods embedded in commercially available software. However, with the rising cost of software, a research revolution in CR is becoming limited. In this work, a CR system is developed using open-source and free software, SciLab. It is the most desirable choice than other compensated software. CR experiments have been done using ANN. The topologies of the neural network varied to recognize ten numerals. The neural network is applied to classify the character with the online backpropagation algorithm by changing the weights for each input online. The results reveal a lower error and the system’s accuracy of 99.92%. With standard backpropagation (batch version) while varying weights after a particular batch. An error is comparatively more, and the system’s output accuracy of 99.62% for the same topology. The application of pre-processing techniques to the given images with topology optimization. The image recognition accuracy is increased by 100%. The system provided optimum results with a topology of 135–100-10. So, the online backpropagation algorithm is more accurate than the standard batch version and should be adopted. Other CR research models can be developed with the SciLab Toolboxes at no cost and with maximum system accuracy.

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Authors and Affiliations

  1. Department of Electronics and Communication, Ludhiana College of Engineering and Technology, Katani Kalan, Ludhiana, Punjab, 141113, India

    Priya Darshni

  2. Department of Electronics and Communication Engineering, NITTTR Chandigarh, Chandigarh, India

    Balwinder Singh Dhaliwal

  3. Department of Mechanical and Production Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

    Raman Kumar

  4. Department of Mechanical Engineering, Edo University Iyamho, Okpella, Edo State, Nigeria

    Vincent Aizebeoje Balogun

  5. Mechanical Engineering, National University of Singapore, Singapore, Singapore

    Sunpreet Singh

  6. Mechanical Engineering, Imperial College, Exhibition Rd., London, SW7 2AZ, UK

    Cătălin Iulian Pruncu

  7. Design, Manufacturing & Engineering Management, University of Strathclyde, Glasgow, Scotland, G1 1XJ, UK

    Cătălin Iulian Pruncu

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  1. Priya Darshni
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  2. Balwinder Singh Dhaliwal
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  3. Raman Kumar
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  4. Vincent Aizebeoje Balogun
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Correspondence to Raman Kumar or Cătălin Iulian Pruncu.

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Darshni, P., Dhaliwal, B.S., Kumar, R. et al. Artificial neural network based character recognition using SciLab. Multimed Tools Appl 82, 2517–2538 (2023). https://doi.org/10.1007/s11042-022-13082-w

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