Optical Character Recognition (OCR) systems are frequently used to digitise text, but often produce noisy results, especially with historical, poor-quality or multilingual data.Despite advances in OCR technology, post-processing remains a significant bottleneck.We propose TOOL (Treating OCR Output as a Language), a new approach that understands OCR correction as a machine translation task.By treating noisy OCR text as a language in its own right, TOOL employs sequenceto-sequence models like Marian to translate it into clean, standardised text.This method is scalable, model-independent and language-flexible.We demonstrate this approach by translating "OCR German" to Standard German from around 1871 to the present day, improving accuracy at the token level by using matched training pairs of OCR output and base text.

The importance of federated information retrieval (FIR) is growing in humanities research.Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised collection of documents, FIR treats each information system as an independent source with its own unique characteristics.Searching these systems together as a centralised source results in lower precision in humanities research, even when the research data itself is structured and stored according to standardised guidelines such as EpiDoc, and requires the need to be able to trace the origin of records to avoid incorrect historical conclusions.Matching of queries against all data sets in each source is proving less effective.A global search index that enables traceable matching of key values deemed relevant would provide a more robust solution here.In this article, we propose a solution that introduces a novel EpiDoc data matching procedure, facilitating traceable FIR across distinct epigraphic sources.

We present a formal framework for media interpretation that leverages low-level information extraction to a higher level of abstraction in order to support semantics-based information retrieval for the Semantic Web. The overall goal of the framework is to provide high-level content descriptions of documents for maximizing precision and recall of semantics-based information retrieval.