# Data Incubation -- Synthesizing Missing Data for Handwriting Recognition

arXiv: https://arxiv.org/abs/2110.07040 · HTML: https://mlsanity.com/paper/2110.07040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Separating Style and Content for Generalized Style Transfer](https://mlsanity.com/paper/1711.06454/) (2017)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems](https://mlsanity.com/paper/2011.11564/) (2020)
- [DeepWriting: Making Digital Ink Editable via Deep Generative Modeling](https://mlsanity.com/paper/1801.08379/) (2018)
- [Handwriting Transformers](https://mlsanity.com/paper/2104.03964/) (2021)
- [You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation](https://mlsanity.com/paper/2005.07157/) (2020)
- [Generating Handwriting via Decoupled Style Descriptors](https://mlsanity.com/paper/2008.11354/) (2020)
- [Fast Multi-language LSTM-based Online Handwriting Recognition](https://mlsanity.com/paper/1902.10525/) (2019)
- [Training Neural Speech Recognition Systems with Synthetic Speech Augmentation](https://mlsanity.com/paper/1811.00707/) (2018)
- [Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models](https://mlsanity.com/paper/2110.02891/) (2021)
- [Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution](https://mlsanity.com/paper/2007.00051/) (2020)

## Built on by (most cited first, 1 of 1)

- [Sampling and Ranking for Digital Ink Generation on a tight computational budget](https://mlsanity.com/paper/2306.03103/) (2023)

## Abstract (arXiv)

In this paper, we demonstrate how a generative model can be used to build a better recognizer through the control of content and style. We are building an online handwriting recognizer from a modest amount of training samples. By training our controllable handwriting synthesizer on the same data, we can synthesize handwriting with previously underrepresented content (e.g., URLs and email addresses) and style (e.g., cursive and slanted). Moreover, we propose a framework to analyze a recognizer that is trained with a mixture of real and synthetic training data. We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only. Overall, we achieve a 66% reduction in Character Error Rate.