The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies. However, it is now necessary to reflect on the experiences and the practices developed by research teams. Our use of the Transkribus platform since 2018 has led us to search for the most significant ways to improve the performance of our handwritten text recognition (HTR) models which are made to transcribe French handwriting dating from the 17th century. This article therefore reports on the impacts of creating transcribing protocols, using the language model at full scale and determining the best way to use base models in order to help increase the performance of HTR models. Combining all of these elements can indeed increase the performance of a single model by more than 20% (reaching a Character Error Rate below 5%). This article also discusses some challenges regarding the collaborative nature of HTR platforms such as Transkribus and the way researchers can share their data generated in the process of creating or training handwritten text recognition models.

翻译：手写识别技术的出现为遗产研究领域带来了新的可能性。然而，当前有必要反思研究团队已积累的经验与实践方法。自2018年起，我们对Transkribus平台的应用促使我们探索提升手写文本识别（HTR）模型性能的关键途径——这些模型旨在转录17世纪的手写法语文献。本文报告了以下因素的影响：制定转录协议、全面运用语言模型以及确定基础模型的最佳使用方式，以期提升HTR模型性能。综合运用这些要素可使单一模型性能提升逾20%（字符错误率低于5%）。本文同时探讨了HTR平台（如Transkribus）协作特性所面临的挑战，以及研究人员在创建或训练手写文本识别模型过程中共享所生成数据的方式。