Integrating Fuzzy Logic and Fractional-Order Operators in Convolutional Neural Networks for Handwritten Digits and Characters Recognition

Handwritten Digit Recognition Based on Convolutional Neural Network and Its Application in Overlapping Image Processing

HYBRID DEEP LEARNING-CROW SEARCH ALGORITHM FOR ACCURATE ARABIC HANDWRITTEN DIGIT RECOGNITION

This paper proposes a hybrid deep learning framework for Arabic handwritten digit recognition by optimizing Convolutional Neural Network (CNN) hyperparameters using the Crow Search Algorithm (CSA). Due to the high variability and structural complexity of Arabic handwritten digits, achieving optimal CNN performance requires efficient and automatic hyperparameter tuning. In the proposed approach, CSA is employed to optimize key CNN hyperparameters, including filter size, number of filters, mini-batch size, and learning rate, with the objective of minimizing classification error. The MADBase dataset is used for evaluation, and preprocessing steps such as normalization, image reshaping, one-hot encoding, noise reduction, and data shuffling are applied to enhance training efficiency and model robustness. The CNN architecture is trained using the optimized hyperparameters obtained through CSA iterations. Experimental results show that the proposed CSA-optimized CNN achieves 99% accuracy on the testing set, demonstrating strong generalization capability and stability. The findings confirm that CSA effectively improves CNN performance while eliminating the need for manual hyperparameter tuning, making the framework suitable for other image classification tasks.

Attention-enhanced CNN-KAN: a hybrid approach for Arabic handwritten character recognition

Handwritten Digit Recognition Using Deep Learning

The Handwritten Digit Recognition System is a machine learning and deep learning–based project developed to accurately identify handwritten numerical digits from input images. The main objective of this project is to recognize digits ranging from 0 to 9 by using image processing techniques and a Convolutional Neural Network (CNN) model trained on the MNIST dataset. In this system, the handwritten digit image is first captured and preprocessed through steps such as grayscale conversion, resizing, normalization, and noise reduction to improve prediction accuracy. The processed image is then passed to the trained CNN model, which extracts important features and classifies the digit into the corresponding numerical class. The project uses backpropagation for learning, Adam optimizer for efficient weight optimization, and Softmax activation function in the output layer for multi-class classification. The trained model provides high accuracy and fast prediction results, making the system suitable for real-time applications. This project demonstrates the practical implementation of deep learning in image recognition and can be further extended for applications such as automatic form processing, postal code recognition, and bank cheque digit identification.

Handwritten Character Recognition Using Deep Learning

Handwritten Character Recognition (HCR) is a significant area of research in the field of pattern recognition and computer vision, aiming to automatically identify and convert handwritten text into machine readable digital formats. Traditional recognition methods often struggle with variations in writing styles, character shapes, and image quality. To address these challenges, this research proposes a deep learning-based handwritten character recognition system that leverages Convolutional Neural Networks (CNNs) for automatic feature extraction and classification. The proposed model is trained on a large dataset of handwritten characters and incorporates preprocessing techniques such as image normalization, noise removal, and segmentation to improve recognition accuracy. Deep learning enables the system to learn complex patterns and hierarchical features directly from raw image data, eliminating the need for manual feature engineering. Experimental results demonstrate that the proposed approach achieves high accuracy, robustness, and generalization across different handwriting styles. The system effectively recognizes handwritten alphabets, numerals, and special characters, making it suitable for applications such as document digitization, postal mail sorting, bank cheque processing, educational assessment systems, and historical manuscript preservation. The study highlights the effectiveness of deep learning techniques in enhancing recognition performance and reducing classification errors compared to conventional machine learning methods. Future enhancements may include multilingual character recognition, real-time processing capabilities, and integration with advanced architectures such as transformers and recurrent neural networks to further improve accuracy and efficiency. Index Terms - Handwritten Character Recognition, Deep Learning, Convolutional Neural Network, Image Processing, Pattern Recognition, Optical Character Recognition, Machine Learning.

Improved Recognition of Telugu Handwritten Characters Through Synthetic Data Generation and Attention-Based Modelling

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.