A TEI-based Layout Annotation System for a Deeper Automatic Encoding of Documents

Automatic Text Recognition (ATR) has become a key component of digital editorial workflows, enabling the conversion of scanned documents into TEI. Beyond text recognition, ATR pipelines rely on Document Layout Analysis (DLA) to segment pages into labeled zones that support document reconstruction. Controlled vocabularies such as SegmOnto have already been widely used for this purpose, notably in projects like Gallicorpora and SETA. This paper presents LADaS (Layout Analysis Dataset with SegmOnto), an extension of SegmOnto designed for deeper alignment with the TEI Guidelines. LADaS introduces a two-level annotation system combining SegmOnto broad layout zones with visually identifiable subzones mapped on the TEI. The paper describes the process of building this controlled vocabulary and its documentation, as well as the associated annotated dataset and a proposed pipeline to convert historical scanned documents into deeper encoded TEI files.