This paper presents our recent developments in the field of automatic processing of sign language corpora using the Hamburg Sign Language Annotation System (HamNoSys). We designed an automated tool to convert HamNoSys annotations into numerical labels for defined initial features of body and hand positions. Our proposed numerical multilabels greatly simplify the structure of HamNoSys annotation without significant loss of gloss meaning. These numerical multilabels can potentially be used to feed the machine learning models, which would accelerate the development of vision-based sign language recognition. In addition, this tool can assist experts in the annotation process to help identify semantic errors. The code and sample annotations are publicly available at https://github.com/hearai/parse-hamnosys.

本文介绍了使用Hamburg手语注释系统（HamNoSys）对手语语料库进行自动处理的最新进展。我们设计了自动化工具，将 HamNoSys 注释转换为身体和手部位置的定义初始特征的数字标签。我们提出的数字多标签可以潜在地用于馈送机器学习模型，这将加速视觉手语识别的发展。此外，该工具可以协助专家进行注释过程，并帮助识别语义错误。

使用数字化多标签为基础的计算机友好手语转录系统