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Among mass digitization methods, double-keying is considered to be the one with the lowest error rate. This method requires two independent transcriptions of a text by two different operators. It is particularly well suited to historical texts, which often exhibit deficiencies like poor master copies or other difficulties such as spelling variation or complex text structures. Providers of data entry services using the double-keying method generally advertise very high accuracy rates (around 99.95% to 99.98%). These advertised percentages are generally estimated on the basis of small samples, and little if anything is said about either the actual amount of text or the text genres which have been proofread, about error types, proofreaders, etc. In order to obtain significant data on this problem it is necessary to analyze a large amount of text representing a balanced sample of different text types, to distinguish the structural XML/TEI level from the typographical level, and to differentiate between various types of errors which may originate from different sources and may not be equally severe. This paper presents an extensive and complex approach to the analysis and correction of double-keying errors which has been applied by the DFG-funded project “Deutsches Textarchiv” (German Text Archive, hereafter DTA) in order to evaluate and preferably to increase the transcription and annotation accuracy of double-keyed DTA texts. Statistical analyses of the results gained from proofreading a large quantity of text are presented, which verify the common accuracy rates for the double-keying method.
Numerous high-quality primary text sources—in the context of the curation project described here, this means full-text transcriptions (and corresponding image scans) of German works originating from the 15th to the 19th centuries—are scattered among the web or stored remotely. E.g., transcriptions of historical sources are stored locally on degrading recording media and cannot be found, let alone accessed by third parties. Additionally, idiosyncratic, project-specific markup conventions and uncommon, out-of-date or inflexible storage formats often hinder further usage and analysis of the data. Often, textual resources are accompanied by scarce, insufficient or inaccurate bibliographic information, which is only one further reason why valuable resources, even if available on the web, remain undiscovered by and are of little use to the wider research community. The integration of these dispersed primary text sources into the sustainable, web and centres-based research infrastructure of CLARIN-D will be an important step to solve this problem. The Full Paper illustrates an exemplary approach taken by the »Deutsches Textarchiv« (DTA; www.deutschestextarchiv.de) at the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW) to integrate dispersed textual resources and corresponding image scans from various sources into a large historical text corpus of its own and to insert these into the infrastructure of CLARIN-D.