# A Study of Augmentation Methods for Handwritten Stenography Recognition

arXiv: https://arxiv.org/abs/2303.02761 · HTML: https://mlsanity.com/paper/2303.02761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images](https://mlsanity.com/paper/2003.02567/) (2020)
- [Adversarial Generation of Handwritten Text Images Conditioned on Sequences](https://mlsanity.com/paper/1903.00277/) (2019)
- [Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes](https://mlsanity.com/paper/2110.05909/) (2021)
- [SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators](https://mlsanity.com/paper/2105.10528/) (2021)
- [HWNet v2: An Efficient Word Image Representation for Handwritten Documents](https://mlsanity.com/paper/1802.06194/) (2018)
- [Matching Handwritten Document Images](https://mlsanity.com/paper/1605.05923/) (2016)
- [Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections](https://mlsanity.com/paper/1703.07645/) (2017)

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

- [Handwritten Stenography Recognition and the LION Dataset](https://mlsanity.com/paper/2308.07799/) (2023)

## Abstract (arXiv)

One of the factors limiting the performance of handwritten text recognition (HTR) for stenography is the small amount of annotated training data. To alleviate the problem of data scarcity, modern HTR methods often employ data augmentation. However, due to specifics of the stenographic script, such settings may not be directly applicable for stenography recognition. In this work, we study 22 classical augmentation techniques, most of which are commonly used for HTR of other scripts, such as Latin handwriting. Through extensive experiments, we identify a group of augmentations, including for example contained ranges of random rotation, shifts and scaling, that are beneficial to the use case of stenography recognition. Furthermore, a number of augmentation approaches, leading to a decrease in recognition performance, are identified. Our results are supported by statistical hypothesis testing. Links to the publicly available dataset and codebase are provided.