# StackMix and Blot Augmentations for Handwritten Text Recognition

arXiv: https://arxiv.org/abs/2108.11667 · HTML: https://mlsanity.com/paper/2108.11667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Decoupled Attention Network for Text Recognition](https://mlsanity.com/paper/1912.10205/) (2019)
- [Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition](https://mlsanity.com/paper/1903.07377/) (2019)
- [SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data](https://mlsanity.com/paper/2012.04846/) (2020)
- [OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold](https://mlsanity.com/paper/2006.07491/) (2020)
- [End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network](https://mlsanity.com/paper/2012.03868/) (2020)
- [A Scalable Handwritten Text Recognition System](https://mlsanity.com/paper/1904.09150/) (2019)
- [Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network](https://mlsanity.com/paper/2012.04961/) (2020)
- [Attention-based Fully Gated CNN-BGRU for Russian Handwritten Text](https://mlsanity.com/paper/2008.05373/) (2020)
- [Mixup Without Hesitation](https://mlsanity.com/paper/2101.04342/) (2021)
- [Handwriting Recognition of Historical Documents with few labeled data](https://mlsanity.com/paper/1811.07768/) (2018)
- [Are 2D-LSTM really dead for offline text recognition?](https://mlsanity.com/paper/1811.10899/) (2018)
- [No Padding Please: Efficient Neural Handwriting Recognition](https://mlsanity.com/paper/1902.11208/) (2019)
- [Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling](https://mlsanity.com/paper/1703.00096/) (2017)

## Built on by (most cited first, 2 of 2)

- [Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques](https://mlsanity.com/paper/2507.06275/) (2025)
- [Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture](https://mlsanity.com/paper/2111.10974/) (2021)

## Abstract (arXiv)

This paper proposes a handwritten text recognition(HTR) system that outperforms current state-of-the-artmethods. The comparison was carried out on three of themost frequently used in HTR task datasets, namely Ben-tham, IAM, and Saint Gall. In addition, the results on tworecently presented datasets, Peter the Greats manuscriptsand HKR Dataset, are provided.The paper describes the architecture of the neural net-work and two ways of increasing the volume of train-ing data: augmentation that simulates strikethrough text(HandWritten Blots) and a new text generation method(StackMix), which proved to be very effective in HTR tasks.StackMix can also be applied to the standalone task of gen-erating handwritten text based on printed text.