Workshop Schedule
September 3, 2026 — Room EI9 — ICDAR 2026, Vienna
Bold denotes the presenting author.
- 13:30 Welcome & Greetings
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14:00
Page-Level Synthetic Russian Handwriting via Decomposed Diffusion
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14:20
Test-Time Adaptation of Frozen Scene-Text Recognizers to Documents and Handwriting
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14:40
Age-Adaptive Handwriting Reconstruction from an IMU-Based Digital Pen through Shared Representations and Domain-Specific Heads
- 15:00 Coffee Break
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15:30
When Low CER is Not Enough: An Analysis of Hallucinations in Vision-Language OCR Systems on Historical Uruguayan Documents
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15:50
Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering
- 16:10 Discussion Session — Parallel group discussions about subjects related to the presented papers and wrap-up
- 17:00 Final Remarks & Closing
Scope and Motivation
Document Analysis (DA) technologies are becoming increasingly pervasive in our daily lives due to the digitalization of documents (both in the cultural and industrial domains) and the widespread use of paper tablets, pads, and smartphones to take notes and sign documents. High-performing DA algorithms are needed that are able to deal with digitized documents from different writers, in different languages, and with different visual characteristics.
As a result, modern DA systems must be able not only to generalize, but also to adapt efficiently to new domains and users, often under limited supervision or data availability. In this context, domain adaptation and automatic personalization play a central role in improving the robustness and usability of DA techniques.
Recent advances in large pre-trained language and multimodal models enable new forms of lightweight domain adaptation of generic systems. Strategies such as prompt engineering, few-shot and in-context learning, structured output generation, and task-specific post-processing allow adapting these models to specific domains without full retraining.
Furthermore, in contexts involving sensitive information, privacy-preserving solutions and lightweight adaptation strategies that can be performed onboard personal devices are crucial.
This workshop aims to gather expertise and novel ideas for personalized DA tasks, welcoming contributions on training and adaptation strategies of specialized models, generic Large Language Models (LLMs), and Large Multimodal Models (LMMs).
Workshop Topics
We call for submissions addressing, but not limited to, the following topics:
Important Dates
Paper Submission
- Springer Lecture Notes in Computer Science (LNCS) format.
- Short papers: 2–8 pages (including figures and references).
- Long papers: 8–17 pages (including figures and references).
- Double-blind review process. Do not include names, affiliations, or acknowledgements.
- Camera-ready files: Springer prepares proceedings from LaTeX source files, not only PDFs.
We use the Microsoft CMT Platform for managing submissions and peer reviews.
Go to CMT SystemAll accepted papers will be included in the main ICDAR workshop proceedings volume and presented orally.
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.