Handwritten Code Recognition

Handwritten code recognition aims to automatically translate handwritten code into machine-readable format, addressing challenges in computer science education and historical document analysis. Current research focuses on improving accuracy using deep learning models like convolutional neural networks (CNNs), often incorporating techniques such as transfer learning, attention mechanisms, and data augmentation strategies (e.g., using generative models to expand training datasets). These advancements are crucial for broadening access to computer science education and efficiently digitizing large collections of handwritten documents across various languages, including those with complex scripts.