ADAPDA

Document Analysis (DA) technologies are becoming increasingly pervasive in our daily life 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. In this respect, high-performing DA algorithms are needed that are able to deal with digitalized documents from different writers, in different languages (including ancient languages, modern slang terms, or writer-preferred abbreviations and symbols), and with different visual characteristics (due to the paper support and the writing tool), often very peculiar to the application domain. 

In this respect, domain adaptation and automatic personalization strategies are worth investigating to boost the performance of DA techniques in the scenarios mentioned above, which are of great cultural, practical, and economic interest. Nonetheless, in some application contexts, writer-specific data may contain sensitive information (either personal or business-related). In the sight of this, specific privacy-preserving solutions and lightweight adaptation strategies that can be performed onboard on personal devices must be considered when designing personalized DA techniques.

This workshop aims at gathering expertise and novel ideas for personalized DA tasks such as, for example, Handwritten Text Recognition, Handwritten Text Generation, Writer Identification, Writer and Signature Verification, and Handwriting Analysis. In particular, it welcomes contributions on training and adaptation strategies of writer, language, and visual-specific models, new benchmarks, and data collection strategies to explore the mentioned tasks in a personalized setting, as well as related works on the personalized DA topic.

 The workshop calls for submissions addressing, but not limited to, the following topics:

○ Adaptation for on-line Handwritten Text Recognition

○ Adaptation for off-line Handwritten Text Recognition

○ Adaptation for Handwritten Text Generation

○ Adaptation for Document Analysis System

○ Domain-specific text and symbol recognition

○ Adaptation for Human document interaction adaptation for Mobile text recognition

○ Domain-specific document analysis

○ Privacy-preserving domain adaptation of handwritten data

○ Writer, Language, and Visual adaptation of Document Analysis models

○ Novel benchmarks and datasets for personalized Document Analysis