Handwritten Mathematical Expression

Handwritten mathematical expression recognition (HMER) aims to automatically translate images of handwritten mathematical formulas into machine-readable formats like LaTeX. Current research heavily utilizes encoder-decoder architectures, often incorporating transformers or recurrent neural networks, with a focus on improving accuracy by explicitly modeling spatial relationships between symbols and incorporating syntactic information through techniques like attention mechanisms and parsing trees. Advances in HMER are crucial for applications in automated grading, digital education, and document digitization, driving the development of larger datasets and more robust algorithms.