Handwritten Text Image

Handwritten text image analysis focuses on automatically understanding and processing images of handwritten text, aiming to improve tasks like character recognition, document analysis, and writer identification. Current research emphasizes developing robust deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like BiLSTM), and transformers, often incorporating techniques like attention mechanisms and multi-scale feature fusion to handle variations in handwriting styles and image quality. These advancements are crucial for digitizing historical archives, improving accessibility for people with disabilities, and enabling new applications in forensic science and document authentication.