Tim Raven, Tim Hallyburton and Gernot A. Fink
Proc. Int. Conf. on Document Analysis and Recognition, 2026, To appear.
Vienna, Austria
We introduce FormWR, a challenging large-scale dataset for writer retrieval and a supervised end-to-end full-image retrieval method using a novel learnable feature aggregation module, X-VLAD. FormWR contains almost 400k pages of application-for-assistance forms attributed to almost 100k distinct writer proxies. Documents are dominated by printed templates, and further reflect real-world uncertainty through multi-hand contamination and pages with sparse handwriting. Our model jointly trains a feature extractor and aggregator. It is pretrained on sets of $32\times32$ handwriting-centered patches and then fine-tuned on full-page images. We report a comprehensive large-scale evaluation on FormWR. Further, we demonstrate transfer to fragment-level retrieval on the HisFragIR20 competition benchmark. We achieve new state-of-the-art performance with 97.9\% Top-1 accuracy and 78.8\% mAP, improving both metrics by large margins (+12.7\% Top-1 and +21.6\% mAP).