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arXiv · 2606.05018

Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives

Marco Peer·Thomas Gorges·Mathias Seuret·Vincent Christlein·Andreas Fischer
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Abstract

Popular initiatives and referendums are central to Swiss democracy, yet the validation of handwritten signature lists remains a labor-intensive manual process. This paper investigates the potential of automated document analysis methods, including OCR and AI-based handwriting analysis, to support this task. We propose a pipeline combining template-based line segmentation with text recognition and writer retrieval techniques, evaluated on a dataset of 443 handwritten entries from 418 writers. Results show that OCR struggles with out-of-vocabulary handwriting, with a CER of 29.6% for first names. In contrast, writer retrieval performs more robustly, reaching an mAP of 50.6%. Furthermore, our experiments indicate that off-the-shelf OCR systems are not sufficiently reliable for transcription of handwritten signature data, particularly for short, out-of-vocabulary entries such as names or addresses. However, writer retrieval methods can effectively identify visually similar entries across signature lists, making them a suitable tool for supporting the detection of potential duplicate submissions based on handwriting similarity.

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Marco Peer, Thomas Gorges, Mathias Seuret, Vincent Christlein, Andreas Fischer. 2026-06-03. Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives. https://arxiv.org/abs/2606.05018

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