Handwritten Text Recognition from Crowdsourced Annotations
Solène Tarride Tristan Faine Mélodie Boillet Harold Mouchère Christopher Kermorvant
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.