Optimized Type - 3 Fuzzy Logic for Robust Learning Algorithm ‎in OCR Recognition

  • Authors

    • P. Sweetlin Research Scholar, Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, ‎Chennai, Tamil Nadu, India
    • G. Jayalalitha Professor, Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil ‎Nadu, India
    https://doi.org/10.14419/608p4p44

    Received date: May 2, 2025

    Accepted date: May 27, 2025

    Published date: July 8, 2025

  • OCR; Type-3 Fuzzy Logic; Robust Learning Algorithm; Educational System; Recognition Accuracy; Optimization and Noise Handling

  • Abstract

    Optimized Type-3 Fuzzy logic system integrated with robust learning algorithm and enhanced with OCR performances. This approach is ‎specifically within the context of educational applications. OCR played an important role in converting printed educational materials into ‎digital formats. The reason for selecting fuzzy logic in the education sector is not only learning for the decision-making process. Fuzzy logic ‎is a more realistic and flexible evaluation. Results as more consistent, objectives. Type-1 fuzzy logic systems have single-valued ‎membership functions, they only handle basic-level operations. Type-2 fuzzy logic is computationally more complex with requires larger ‎resources to process in educational applications. To overcome this issue Optimized Type-3 Fuzzy logic combined the type-1 and type-2 ‎fuzzy logic systems with the terms to improve the adaptability and performance. Introducing the optimized Type-3 fuzzy logic algorithm ‎used PSO noted as PSO-OT3FL-RLA, helps to improve the accurateness and strength of the OCR system. This system also incorporates ‎uncertainty in the recognition process and ensures adaptable recognition outcomes. The proposed model is an advanced learning algorithm ‎integrated with fuzzy logic that effectively handles noisy input data. PSO technique ensures efficient exploration and maintains the balance ‎between the quality of the result and computational efficiency. Utilize the proposed approach performed through the traditional OCR ‎methods in terms of accuracy and computational error rate. After evaluating the overall performance of the proposed model, we provide a ‎result of 94.67%. It provides a promising solution for OCR-based applications in educational sectors‎.

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    Sweetlin , P. ., & Jayalalitha , G. . (2025). Optimized Type - 3 Fuzzy Logic for Robust Learning Algorithm ‎in OCR Recognition. International Journal of Basic and Applied Sciences, 14(SI-1), 111-121. https://doi.org/10.14419/608p4p44