Handwriting Extraction and Analysis of Signature Lists in Swiss Popular Initiatives
For Swiss authorities, this work explores automation of signature validation, but results show OCR is unreliable and writer retrieval offers only moderate performance, making the contribution incremental.
The paper evaluates automated methods for extracting and analyzing handwritten signature lists in Swiss popular initiatives, finding that OCR achieves a CER of 29.6% for first names, while writer retrieval reaches an mAP of 50.6%, indicating that writer retrieval is more robust for detecting duplicate submissions.
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.