<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt" xml:space="preserve">Next, we converted segmented images into machine-readable text using OCR with the frak2021 model (Mannheim University Library 2021) and applied post-correction using a </hi> <ref xmlns="http://www.tei-c.org/ns/1.0" target="https://huggingface.co/hmbyt5-preliminary"> <hi rend="color(1155CC)" style="font-size:11pt">hmbyt5-preliminary</hi> </ref> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt" xml:space="preserve"> model fine-tuned on the IDCAR2019-POCR dataset for OCR correction (Rigaud et al. 2019). To evaluate the quality of the OCRed text and the OCR correction output, we created a gold standard dataset by manually correcting the text in the ads, using the Transkribus platform (Kahle et al. 2017). This provides us with insights into common error patterns in the OCR stage, such as confusing letters </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" style="font-size:11pt">n</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt" xml:space="preserve"> and </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" style="font-size:11pt">u</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt" xml:space="preserve"> or </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" style="font-size:11pt">f</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt" xml:space="preserve"> and </hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" style="font-size:11pt">ſ</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:11pt">, which helped us improve the quality of the text in the post-correction stage.</hi>