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
– unlearning for document image classification
– unlearning for handwritten text recognition
– unlearning for document visual question answering
– unlearning for multimodal document understanding
Robustness in document image recognition systems
– adversarial attacks & defenses
– robustness to data noise, layout variation, and handwriting styles
Privacy in document image understanding
– differential privacy for document image datasets
– membership inference and training data leakage prevention
– privacy-preserving OCR and document representation learning
Explainability and interpretability
– explaining document model decisions at pixel, region, and semantic levels
– visualization tools for understanding features learned from document images
Evaluation, benchmarks, and best practices
– metrics and datasets for assessing unlearning, privacy, robustness, and explainability
– standardized evaluation pipelines for trustworthy document intelligence
Applications and case studies
– legal, financial, medical, and government documents
– lifelong learning and compliance-driven document AI