Optimized Type - 3 Fuzzy Logic for Robust Learning Algorithm in OCR Recognition
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Keywords:
OCR; Type-3 Fuzzy Logic; Robust Learning Algorithm; Educational System; Recognition Accuracy; Optimization and Noise HandlingAbstract
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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