  Identity document (ID) image analysis has become essential for many online
services, like bank account opening or insurance subscription. In recent years,
much research has been conducted on subjects like document localization, text
recognition and fraud detection, to achieve a level of accuracy reliable enough
to automatize identity verification. However, there are only a few available
datasets to benchmark ID analysis methods, mainly because of privacy
restrictions, security requirements and legal reasons.
  In this paper, we present the DocXPand-25k dataset, which consists of 24,994
richly labeled IDs images, generated using custom-made vectorial templates
representing nine fictitious ID designs, including four identity cards, two
residence permits and three passports designs. These synthetic IDs feature
artificially generated personal information (names, dates, identifiers, faces,
barcodes, ...), and present a rich diversity in the visual layouts and textual
contents.
  We collected about 5.8k diverse backgrounds coming from real-world photos,
scans and screenshots of IDs to guarantee the variety of the backgrounds. The
software we wrote to generate these images has been published
(https://github.com/QuickSign/docxpand/) under the terms of the MIT license,
and our dataset has been published
(https://github.com/QuickSign/docxpand/releases/tag/v1.0.0) under the terms of
the CC-BY-NC-SA 4.0 License.
