Integrating Fuzzy Logic and Fractional-Order Operators in Convolutional Neural Networks for Handwritten Digits and Characters Recognition
TL;DR
This study focuses on enhancing handwritten Devanagari character recognition using deep learning models, specifically fine-tuned Convolutional Neural Networks (CNNs), combined with hybrid mathematical methods for image enhancement, proposing a fuzzy-enabled Power-Law transformation for image enhancement.
Abstract
Handwritten character recognition is vital for document digitization and autonomous reading systems. This study focuses on enhancing handwritten Devanagari character recognition using deep learning models, specifically fine-tuned Convolutional Neural Networks (CNNs), combined with hybrid mathematical methods for image enhancement. Devanagari script, used for several South Asian languages, presents challenges due to its complexity and structural variations. To improve recognition accuracy, we propose a fuzzy-enabled Power-Law transformation for image enhancement, along with other techniques like Grunwald- Letnikov Fractional Differentiation (GLFD) and Atangana-Baleanu-Riemann (ABR). Experimental results show that the CNN model with Power-Law+ Fuzzy enhancement achieves the highest accuracy (98.02%), surpassing even MobileNetV2 (92.86%). The same method also yields impressive performance on the MNIST dataset (99.15% accuracy), demonstrating its effectiveness across different scripts. These findings highlight the benefits of integrating advanced preprocessing with deep learning for improved handwriting recognition, offering practical applications in multilingual document processing and OCR-based automation.