# Handwritten image augmentation

arXiv: https://arxiv.org/abs/2308.13791 · HTML: https://mlsanity.com/paper/2308.13791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection](https://mlsanity.com/paper/1903.12564/) (2019)

## Abstract (arXiv)

In this paper, we introduce Handwritten augmentation, a new data augmentation for handwritten character images. This method focuses on augmenting handwritten image data by altering the shape of input characters in training. The proposed handwritten augmentation is similar to position augmentation, color augmentation for images but a deeper focus on handwritten characters. Handwritten augmentation is data-driven, easy to implement, and can be integrated with CNN-based optical character recognition models. Handwritten augmentation can be implemented along with commonly used data augmentation techniques such as cropping, rotating, and yields better performance of models for handwritten image datasets developed using optical character recognition methods.