Data quality for Numismatics based on Natural language processing and Neural Networks - D4N4

其目的是实施钱币学的研究工具，这些工具也适用于其他利用图像和/或文字并可大量获得的学科。这涉及a)多语言和非统一硬币描述的自然语言处理(NLP)及其与分层图像辞典的组合，以及b)使用深度学习对单个元素和整体组成进行图像识别的方法，深度学习是人工智能(AI)的一个组成部分。这两种方法都已经进行了原型开发和测试，并已经交付了有用的结果，特别是在提高数据质量方面。然而，在部署和其他人随后使用之前，必须对开发的工具进行扩展和改进。这将需要增加数据量，这将通过编制档案馆藏索引和记录因特网上可用的材料来实现。这些工具的使用和数据的丰富将构成一个全面的色雷斯古代钱币语料库的创建，并随后允许对其进行货币和图像分析。该项目具有试点性质，并根据历史景观纳入古希腊钱币类型库的国际努力。在钱币社区中，这些工具的用户圈相应地很大，除此之外，对于从事花瓶、浮雕、宝石等工作的其他古代研究人员来说，也是如此。此外，质量控制的实施，包括上述程序，也是确保大量数据的科学质量的问题，其他项目也可以从中受益。通过提供所使用的数据集以及我们在深度学习领域的结果，我们也为其他计算机科学家提供了测试数据/结果的机会，作为新方法的基准。

The aim is to implement research tools for numismatics that are also suitable for other subjects that make use of images and/or writing and are available in large quantities. This involves methods of a) natural language processing (NLP) for multilingual and non-uniform coin descriptions and their combination with a hierarchical iconographic thesaurus, as well as b) image recognition of individual elements and overall compositions using deep learning, a component of artificial intelligence (AI). Both approaches have already been prototypically developed and tested and have already delivered helpful results, especially in increasing data quality. However, before deployment and subsequent use by others, the tools developed must be expanded and improved upon. This will require an increase in the amount of data, which is to be achieved by indexing archive holdings and recording the material available on the Internet. The implementation of the tools together with the enrichment of the data will constitute the creation of a comprehensive corpus of ancient coinage of Thrace and subsequently allow for its monetary and iconographic analysis.The project has a pilot character and is integrated into the international efforts of a type repository of ancient Greek coins according to historical landscapes. The circle of users for the tools is correspondingly large in the numismatic community and, beyond that, for other researchers of antiquity working on vases, reliefs, gems, etc. Furthermore, the implementation of quality controls, including the above-mentioned procedures, is also a matter of ensuring the scientific quality of the large amounts of data, from which other projects can also benefit.By providing the data sets used, as well as our results in the field of Deep Learning, we also offer the opportunity for other computer scientists to test out the data/results as a benchmark for new approaches.