# Unsupervised Training Data Generation of Handwritten Formulas using Generative Adversarial Networks with Self-Attention

arXiv: https://arxiv.org/abs/2106.09432 · HTML: https://mlsanity.com/paper/2106.09432/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://mlsanity.com/paper/1612.05424/) (2016)
- [cGANs with Projection Discriminator](https://mlsanity.com/paper/1802.05637/) (2018)
- [Image-to-Markup Generation with Coarse-to-Fine Attention](https://mlsanity.com/paper/1609.04938/) (2016)
- [Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/1801.03530/) (2018)
- [Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions](https://mlsanity.com/paper/1901.06763/) (2019)
- [Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training](https://mlsanity.com/paper/1908.11415/) (2019)

## Built on by (most cited first, 3 of 3)

- [Towards Scalable Training for Handwritten Mathematical Expression Recognition](https://mlsanity.com/paper/2508.09220/) (2025)
- [HandwritingAgent: Language-Driven Handwriting Synthesis in Scalable Vector Space](https://mlsanity.com/paper/2606.18788/) (2026)
- [DiffMath: Symbol- and Graph-Aware Latent Diffusion Transformer for Handwritten Mathematical Expression Generation](https://mlsanity.com/paper/2606.19939/) (2026)

## Abstract (arXiv)

The recognition of handwritten mathematical expressions in images and video frames is a difficult and unsolved problem yet. Deep convectional neural networks are basically a promising approach, but typically require a large amount of labeled training data. However, such a large training dataset does not exist for the task of handwritten formula recognition. In this paper, we introduce a system that creates a large set of synthesized training examples of mathematical expressions which are derived from LaTeX documents. For this purpose, we propose a novel attention-based generative adversarial network to translate rendered equations to handwritten formulas. The datasets generated by this approach contain hundreds of thousands of formulas, making it ideal for pretraining or the design of more complex models. We evaluate our synthesized dataset and the recognition approach on the CROHME 2014 benchmark dataset. Experimental results demonstrate the feasibility of the approach.