Jul 2026· Chaos and Fractals· 0 citations· 27 references
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.
A serial cascade of lightweight CNN and spectrum normalized GAN and spectrum normalized GAN, integrating CBAM attention mechanism is proposed, integrating CBAM attention mechanism, with good experimental results.
A hybrid deep learning framework for Arabic handwritten digit recognition by optimizing Convolutional Neural Network hyperparameters using the Crow Search Algorithm, confirming that CSA effectively improves CNN performance while eliminating the need for manual hyperparameter tuning, making the framework suitable for other image classification tasks.
Abtisam Abdulelah Salim Azeez· Kufa journal of Engineering· 0 citations
The Attention-Enhanced CNN-KAN (A-CNN-KAN), an innovative hybrid model that combines Convolutional Neural Networks for effective feature extraction, Kolmogorov-Arnold Networks for flexible non-linear pattern modeling, and a spatial attention mechanism to emphasize salient features, is presented.
Alhag Alsayed, Chunlin Li, Mohammed Hafiz et al.· Signal, Image and Video Proc...· 0 citations
The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images by using image processing techniques and a Convolutional Neural Network model trained on the MNIST dataset.
R. Rajesh, P. Sravani· International Journal for Re...· 0 citations
A deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks for automatic feature extraction and classification and highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional machine learning methods.
Shwetha M R Shwetha M R, Kowshik S S Kowshik S S· International Scientific Jou...· 0 citations
This study presents an advanced framework for Telugu handwritten character recognition by integrating Conditional Generative Adversarial Networks (cGANs) with Vision Transformer (ViT) architectures. Critical issues with Telugu scripts, such as intricate character structures, significant inter-writer variability, and a lack of annotated handwritten data, are addressed by the suggested method. While the Vision Transformer utilizes self-attention mechanisms to capture long-range spatial dependencies and global contextual features necessary for accurate recognition, cGAN-based synthetic data augmentation is employed to enhance dataset diversity and mitigate class imbalance. The proposed system outperforms several current CNN-, RNN-, and heuristic-based techniques, achieving character recognition accuracy of 97.89% and word recognition accuracy of 97.34%, as determined through extensive experiments conducted on real and synthetic handwritten datasets. Stable performance under noisy and real-world conditions is further confirmed by robustness analysis. The outcomes confirm the efficacy of integrating transformer-based learning with generative AI, creating a dependable and scalable OCR solution for low-resource Indic scripts, such as Telugu.
Padmavathi Pragada, D. Ch· Engineering Research Express· 0 citations