Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) aims to automatically translate images of handwritten mathematical formulas into structured digital representations, typically LaTeX code. Current research heavily focuses on improving the accuracy and speed of this translation using advanced deep learning architectures, such as transformers and encoder-decoder models, often incorporating tree-based structures to better capture the hierarchical nature of mathematical expressions and employing techniques like attention mechanisms and adversarial learning to enhance performance. These advancements have significant implications for automated grading, document digitization, and accessibility tools, improving efficiency and accuracy in various applications.