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            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
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            <namePart type="given">Stelios</namePart>
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            <namePart type="given">Henk</namePart>
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            <namePart type="given">Antonio</namePart>
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    <abstract>Challenging historical documents still pose significant difficulties for fully automatic layout detection and text recognition, requiring lengthy, demanding correction. We describe our experiences with complex layouts and present our workflow with AdaptOCR, a web-based annotation tool designed to facilitate the efficient transcription and ground-truth annotation of demanding historical documents. Addressing the limitations of existing solutions, AdaptOCR prioritizes a streamlined workflow with an integrated trainable layout and OCR pipeline. The tool uses the PAGE standard to represent document structure and enables the annotation of baselines, regions, text lines and the correction of their transcriptions providing automatic OCR invocation and dictionary-based error detection. Furthermore, it supports flexible annotations with custom element types and attributes to cater to different project requirements. We demonstrate the effectiveness of the workflow and tool in two demanding applications: The transcription of a large corpus of historical printings and the detection / annotation of handwritten artifacts within the private library of the Grimm brothers. In addition, we evaluate the dictionary-based correction and assess the efficiency improvements using AdaptOCR in a pilot study.</abstract>
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%0 Conference Proceedings
%T Human-in-the-Loop Mass Transcription and Ground Truth Annotation for Challenging Historical Documents
%A Fischer, Norbert
%A Puppe, Frank
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F fischer-puppe-2026-human
%X Challenging historical documents still pose significant difficulties for fully automatic layout detection and text recognition, requiring lengthy, demanding correction. We describe our experiences with complex layouts and present our workflow with AdaptOCR, a web-based annotation tool designed to facilitate the efficient transcription and ground-truth annotation of demanding historical documents. Addressing the limitations of existing solutions, AdaptOCR prioritizes a streamlined workflow with an integrated trainable layout and OCR pipeline. The tool uses the PAGE standard to represent document structure and enables the annotation of baselines, regions, text lines and the correction of their transcriptions providing automatic OCR invocation and dictionary-based error detection. Furthermore, it supports flexible annotations with custom element types and attributes to cater to different project requirements. We demonstrate the effectiveness of the workflow and tool in two demanding applications: The transcription of a large corpus of historical printings and the detection / annotation of handwritten artifacts within the private library of the Grimm brothers. In addition, we evaluate the dictionary-based correction and assess the efficiency improvements using AdaptOCR in a pilot study.
%R 10.63317/2m45sutdznvy
%U https://aclanthology.org/2026.lrec-1.559/
%U https://doi.org/10.63317/2m45sutdznvy
%P 7023-7033

Markdown (Informal)

[Human-in-the-Loop Mass Transcription and Ground Truth Annotation for Challenging Historical Documents](https://aclanthology.org/2026.lrec-1.559/) (Fischer & Puppe, LREC 2026)

• Human-in-the-Loop Mass Transcription and Ground Truth Annotation for Challenging Historical Documents (Fischer & Puppe, LREC 2026)

ACL

• Norbert Fischer and Frank Puppe. 2026. Human-in-the-Loop Mass Transcription and Ground Truth Annotation for Challenging Historical Documents. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7023–7033, Palma de Mallorca, Spain. ELRA Language Resource Association.