# Handwritten Text Recognition from Crowdsourced Annotations

arXiv: https://arxiv.org/abs/2306.10878 · HTML: https://mlsanity.com/paper/2306.10878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations](https://mlsanity.com/paper/2110.12088/) (2021)
- [Deep learning from crowds](https://mlsanity.com/paper/1709.01779/) (2017)
- [DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition](https://mlsanity.com/paper/2203.12273/) (2022)
- [Detecting Corrupted Labels Without Training a Model to Predict](https://mlsanity.com/paper/2110.06283/) (2021)
- [To Aggregate or Not? Learning with Separate Noisy Labels](https://mlsanity.com/paper/2206.07181/) (2022)
- [Learning from Crowds with Crowd-Kit](https://mlsanity.com/paper/2109.08584/) (2021)
- [Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions](https://mlsanity.com/paper/1801.02546/) (2018)
- [Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks](https://mlsanity.com/paper/2012.14163/) (2020)
- [Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion](https://mlsanity.com/paper/2207.11327/) (2022)

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

- [Callico: a Versatile Open-Source Document Image Annotation Platform](https://mlsanity.com/paper/2405.01071/) (2024)

## Abstract (arXiv)

In this paper, we explore different ways of training a model for handwritten text recognition when multiple imperfect or noisy transcriptions are available. We consider various training configurations, such as selecting a single transcription, retaining all transcriptions, or computing an aggregated transcription from all available annotations. In addition, we evaluate the impact of quality-based data selection, where samples with low agreement are removed from the training set. Our experiments are carried out on municipal registers of the city of Belfort (France) written between 1790 and 1946. % results The results show that computing a consensus transcription or training on multiple transcriptions are good alternatives. However, selecting training samples based on the degree of agreement between annotators introduces a bias in the training data and does not improve the results. Our dataset is publicly available on Zenodo: https://zenodo.org/record/8041668.