论文标题
HTR模型培训的挑战：来自项目的反馈Donner le Gout de l'Archive a l'Ere numerique
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique
论文作者
论文摘要
手写识别技术的到来为遗产研究提供了新的可能性。但是，现在有必要反思研究团队开发的经验和实践。自2018年以来，我们对Transkribus平台的使用使我们搜索了提高手写文本识别（HTR）模型的性能的最重要方法，该模型可追溯到17世纪的法语手写。因此，本文报告了创建转录协议，使用语言模型的影响，并确定使用基本模型以帮助提高HTR模型的性能的最佳方法。结合所有这些元素的确可以将单个模型的性能提高超过20％（达到5％以下的字符错误率）。本文还讨论了有关Transkribus等HTR平台的协作性质以及研究人员可以在创建或培训手写文本识别模型过程中生成的数据的一些挑战。
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.
