In this course we look at MM-CATTI (Multimodal Computer Assisted Transcription of Text Images) from a general paradigm of Human-Interaction in Pattern Recognition and examine the important challenges and interesting research opportunities entailed the resulting framework. In particular we show how to take direct advantage of the feedback information provided by the user in each interaction step to improve raw recognition performance. We also argue that human interaction naturally entails multimodal processing and show how this fact can lead to improve overall system behavior and usability.

Forensic document examination is a diverse field that includes the analysis and comparison of information that appears in many forms on documents, but we will limit the tutorial to the subject of the determination of authorship.This half-day tutorial will cover the historical and current approach taken by forensic document examiners in handwriting and signature examinations. Topics for the tutorial include: history of the discipline; class versus individual characteristics; variation; statistical approaches; genuineness versus spuriousness; opinions and conclusions.

The tutorial will cover the background issues, challenges and opportunities in the analysis of historical documents and their applications in the Digital Library world. The tutorial is divided in four parts.

The aim of the tutorial “Pattern Recognition Systems – Conception, Evaluation and Improvement” is to lay the foundations and to encourage further discussions on the development of pattern recognition systems, especially for the recognition of printed or handwritten text. Researchers and practitioners working in the field of pattern recognition will be introduced to handwritten text recognition systems in general, steps of a system evaluation process and techniques to improve recognition quality. The objective of this tutorial is to provide a basis for researchers to improve the quality and performance of a recognition system. The presented methods include detailed analysis of a recognition system, relation between structured data and performance of systems, reject / combination strategies, and post-processing approaches. After this tutorial participants should be able to design their own recognition system or improve an existing one.