# Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

arXiv: https://arxiv.org/abs/1801.03530 · HTML: https://mlsanity.com/paper/1801.03530/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2018.

## Built on by (most cited first, 30 of 36)

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- [UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition](https://mlsanity.com/paper/2404.15254/) (2024)
- [When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.11463/) (2022)
- [Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer](https://mlsanity.com/paper/2105.02412/) (2021)
- [CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2207.04410/) (2022)
- [Handwritten Mathematical Expression Recognition via Attention Aggregation based Bi-directional Mutual Learning](https://mlsanity.com/paper/2112.03603/) (2021)
- [PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer](https://mlsanity.com/paper/2407.07764/) (2024)
- [TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2408.08578/) (2024)
- [A Unified Representation Framework for the Evaluation of Optical Music Recognition Systems](https://mlsanity.com/paper/2312.12908/) (2023)
- [PP-FormulaNet: Bridging Accuracy and Efficiency in Advanced Formula Recognition](https://mlsanity.com/paper/2503.18382/) (2025)
- [Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2308.10493/) (2023)
- [ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2405.09032/) (2024)
- [Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention](https://mlsanity.com/paper/2106.09432/) (2021)
- [Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2505.23566/) (2025)
- [CALText: Contextual Attention Localization for Offline Handwritten Text](https://mlsanity.com/paper/2111.03952/) (2021)
- [SEA-Vision: A Multilingual Benchmark for Comprehensive Document and Scene Text Understanding in Southeast Asia](https://mlsanity.com/paper/2603.15409/) (2026)
- [Evaluation of Automated Image Descriptions for Visually Impaired Students](https://mlsanity.com/paper/2106.15553/) (2021)
- [Offline Handwritten Mathematical Recognition using Adversarial Learning and Transformers](https://mlsanity.com/paper/2208.09662/) (2022)
- [Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction](https://mlsanity.com/paper/2308.05820/) (2023)
- [Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2401.00435/) (2023)
- [RFL: Simplifying Chemical Structure Recognition with Ring-Free Language](https://mlsanity.com/paper/2412.07594/) (2024)
- [MMHMER:Multi-viewer and Multi-task for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2502.05557/) (2025)
- [SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels](https://mlsanity.com/paper/2502.07172/) (2025)
- [MFH: Marrying Frequency Domain with Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2507.00430/) (2025)
- [Automated Creation and Human-assisted Curation of Computable Scientific Models from Code and Text](https://mlsanity.com/paper/2202.13739/) (2022)
- [Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2303.07077/) (2023)

## Abstract (arXiv)

Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, we utilize the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. We improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and save the fine-grained details that will be dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.