Stroke Extraction

Stroke extraction, the process of identifying and isolating individual strokes within images, is crucial for various applications, including stroke diagnosis in medical imaging and character recognition in computer vision. Current research focuses on improving the accuracy and efficiency of stroke extraction using deep learning models, particularly convolutional neural networks (CNNs) and, to a lesser extent, transformers, often incorporating techniques like attention mechanisms and data augmentation strategies, including synthetic data generation. These advancements aim to overcome challenges such as noisy data, variations in stroke style, and the need for large annotated datasets, ultimately leading to more robust and reliable automated systems for image analysis and understanding.