{"ID":2877590,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19773","arxiv_id":"2508.19773","title":"The Return of Structural Handwritten Mathematical Expression Recognition","abstract":"Handwritten Mathematical Expression Recognition is foundational for educational technologies, enabling applications like digital note-taking and automated grading. While modern encoder-decoder architectures with large language models excel at LaTeX generation, they lack explicit symbol-to-trace alignment, a critical limitation for error analysis, interpretability, and spatially aware interactive applications requiring selective content updates. This paper introduces a structural recognition approach with two innovations: 1 an automatic annotation system that uses a neural network to map LaTeX equations to raw traces, automatically generating annotations for symbol segmentation, classification, and spatial relations, and 2 a modular structural recognition system that independently optimizes segmentation, classification, and relation prediction. By leveraging a dataset enriched with structural annotations from our auto-labeling system, the proposed recognition system combines graph-based trace sorting, a hybrid convolutional-recurrent network, and transformer-based correction to achieve competitive performance on the CROHME-2023 benchmark. Crucially, our structural recognition system generates a complete graph structure that directly links handwritten traces to predicted symbols, enabling transparent error analysis and interpretable outputs.","short_abstract":"Handwritten Mathematical Expression Recognition is foundational for educational technologies, enabling applications like digital note-taking and automated grading. While modern encoder-decoder architectures with large language models excel at LaTeX generation, they lack explicit symbol-to-trace alignment, a critical li...","url_abs":"https://arxiv.org/abs/2508.19773","url_pdf":"https://arxiv.org/pdf/2508.19773v1","authors":"[\"Jakob Seitz\",\"Tobias Lengfeld\",\"Radu Timofte\"]","published":"2025-08-27T10:58:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}