For machine learning applications, data is only as good as its annotations. MIDV-276 provides: for document localization.
Due to the sensitivity of ID documents, all source images used to create the mock documents in MIDV-500 were obtained from public domain sources (e.g., Wikimedia Commons), making it a secure and ethical resource for researchers. MIDV-2019: Extending the Challenge MIDV-276
: Once digital content goes viral, it can leave a lasting mark on the internet, potentially persisting indefinitely. For machine learning applications, data is only as
Following 2020, research has continued to evolve, with new datasets exploring specific regions—such as MIDV-UP (Pakistan and Iran) and MIDV-LAIT, which focus on diverse, challenging, and non-Latin scripts. Conclusion MIDV-2019: Extending the Challenge : Once digital content
: Documents held at angled or skewed positions.
: Outline the objectives and scope of MIDV-276. What problem does it aim to solve? What are its key components or phases?