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Abstract

Academic research about digital non-Latin script (hereafter: NLS) research data can pose a number of challenges just because the material is from a region where the Latin alphabet was not used. Not all of them are easy to spot. In this paper, I introduce two use cases to demonstrate different aspects of the complex tasks that may be related to NLS material. The first use case focuses on metadata standards used to describe NLS material. Taking the VRA Core 4 XML as example, I will show where we found limitations for NLS material and how we were able to overcome them by expanding the standard. In the second use case, I look at the research data itself. Although the full text digitization of western newspapers from the 20th century usually is not problematic anymore, this is not the case for Chinese newspapers from the Republican era (1912-1949). A major obstacle here is the dense and complex layout of the pages, which prevents OCR solutions to get to the character recognition part. In our approach, we are combining different manual and computational methods, like crowdsourcing, pattern recognition, and neural networks to be able to process the material in a more efficient way. The two use cases illustrate that data standards or processing methods which are established and stable for Latin script material may not always be easily adopted to non-Latin script research data.