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: this https URL

本文研究了多种训练模型的方式来识别手写文本，特别是在存在多个不完整或有噪声的转录版本时，考虑了不同的训练配置和数据选择方法，并在法国贝尔福市的城市登记册上进行了实验，结果表明，计算共识转录或基于多个转录进行训练是有希望的替代方案，但基于注释者之间的一致程度选择训练样本会引入偏差并不能提高结果。