Date: August 21, 2026
Computer Vision Center (CVC), along with Universitat Autònoma de Barcelona, Università degli Studi di Cagliari, Università di Genova and AllRead MLT is organising a workshop at the International Conference on Pattern Recognition (ICPR) 2026.
About the Event
What? Workshop: “Trustworthy Document Understanding: Privacy, Unlearning, Robustness, and Explainability”
When? August 21, 2026
Where? International Convention Center, Lyon, France
Important Dates
- Paper Submission Deadline: 16/05/2026 CET
- Notification to Authors: 11/06/2026 CET
- Camera-ready Deadline: 18/06/2026 CET
- Workshop Date: 21/08/2026 CET
Call for papers
Pattern recognition is the foundation of modern document image understanding, supporting progress in document classification, handwritten text recognition, DocVQA, and multimodal modeling of text, layout, and visual structure. As these systems are increasingly deployed in real-world, high-stakes environments, they must meet new expectations for trust, safety, and regulatory compliance. Current document image models are required to support machine unlearning, to resist imperceptible document forgeries and membership inference attacks, to preserve the privacy of sensitive handwritten or scanned data, and to offer transparent and interpretable decisions. These demands raise fundamental research questions on how models memorise and forget document-specific features, how handwritten text recognition can remain robust under regime changes, and how multimodal document representations should be evaluated from ethical and trustworthiness perspectives.
The workshop invites contributions that advance this emerging area. It specifically welcomes contributions on topics including (but not limited to):
- Machine unlearning in document AI
- Robustness in document image recognition systems
- Privacy in document image understanding
- Explainability and interpretability
- Evaluation, benchmarks, and best practices
- Applications and case studies
Submit!
Learn more about the submission guidelines and submit: https://sites.google.com/view/trustdoc/home