# Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions

arXiv: https://arxiv.org/abs/1901.06763 · HTML: https://mlsanity.com/paper/1901.06763/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [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)
- [Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching](https://mlsanity.com/paper/2409.03643/) (2024)
- [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)
- [PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex](https://mlsanity.com/paper/2105.01846/) (2021)
- [DocFusion: A Unified Framework for Document Parsing Tasks](https://mlsanity.com/paper/2412.12505/) (2024)
- [Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2308.10493/) (2023)
- [Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention](https://mlsanity.com/paper/2106.09432/) (2021)
- [DocTron-Formula: Generalized Formula Recognition in Complex and Structured Scenarios](https://mlsanity.com/paper/2508.00311/) (2025)
- [Evaluation of Automated Image Descriptions for Visually Impaired Students](https://mlsanity.com/paper/2106.15553/) (2021)
- [Enhancing Complex Formula Recognition with Hierarchical Detail-Focused Network](https://mlsanity.com/paper/2409.11677/) (2024)
- [Texo: Formula Recognition within 20M Parameters](https://mlsanity.com/paper/2602.17189/) (2026)

## Abstract (arXiv)

Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the ambiguity and complexity of two-dimensional handwriting. Moreover, the lack of large training data is a serious issue, especially for academic recognition systems. In this paper, we propose pattern generation strategies that generate shape and structural variations to improve the performance of recognition systems based on a small training set. For data generation, we employ the public databases: CROHME 2014 and 2016 of online HMEs. The first strategy employs local and global distortions to generate shape variations. The second strategy decomposes an online HME into sub-online HMEs to get more structural variations. The hybrid strategy combines both these strategies to maximize shape and structural variations. The generated online HMEs are converted to images for offline HME recognition. We tested our strategies in an end-to-end recognition system constructed from a recent deep learning model: Convolutional Neural Network and attention-based encoder-decoder. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our strategies: our hybrid strategy achieved classification rates of 48.78% and 45.60%, respectively, on these databases. These results are competitive compared to others reported in recent literature. Our generated datasets are openly available for research community and constitute a useful resource for the HME recognition research in future.