# Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention

arXiv: https://arxiv.org/abs/2007.10092 · HTML: https://mlsanity.com/paper/2007.10092/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [Syntax-Aware Network for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2203.01601/) (2022)
- [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)
- [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)
- [Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2308.10493/) (2023)
- [MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition](https://mlsanity.com/paper/2404.13667/) (2024)
- [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)
- [Online Gesture Recognition using Transformer and Natural Language Processing](https://mlsanity.com/paper/2305.03407/) (2023)
- [Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network](https://mlsanity.com/paper/2409.11677/) (2024)
- [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)
- [A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions](https://mlsanity.com/paper/2211.02643/) (2022)
- [Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2303.07077/) (2023)
- [Attention Guidance Mechanism for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2403.01756/) (2024)
- [Mask & Match: Learning to Recognize Handwritten Math with Self-Supervised Attention](https://mlsanity.com/paper/2508.06107/) (2025)
- [MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers](https://mlsanity.com/paper/2601.10104/) (2026)
- [Texo: Formula Recognition within 20M Parameters](https://mlsanity.com/paper/2602.17189/) (2026)
- [A Hybrid Vision Transformer Approach for Mathematical Expression Recognition](https://mlsanity.com/paper/2603.07929/) (2026)

## Abstract (arXiv)

Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs). To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention. Specifically, tackling ME with unstable scale in both horizontal and vertical directions, scale augmentation improves the performance of the model on MEs of various scales. An attention-based encoder-decoder network is used for extracting features and generating predictions. In addition, drop attention is proposed to further improve performance when the attention distribution of the decoder is not precise. Compared with previous methods, our method achieves state-of-the-art performance on two public datasets of CROHME 2014 and CROHME 2016.