Recognition Techniques for Whiteboard Notes Written in Roman Script

Marcus Liwicki; Horst Bunke

In: C H Chen. Handbook of Pattern Recognition and Computer Vision. Chapter 3.5, Pages 397-414, ISBN 978-981-4273-38-1, World Scientific, 2010.


In this chapter we describe various methods for the automatic recognition of handwritten whiteboard notes. A handwriting recognition system for Roman Script is usually divided into units which iteratively process the handwritten input data to finally obtain the desired ASCII transcription: the preprocessing, where noise in the raw data is reduced; the normalization, where various steps take place to remove writer-specific characteristics of the handwriting; the feature extraction, where the normalized data is transformed into a sequence of feature vectors; the recognition, where a classifier generates a list of word sequence candidates; and the post-processing, where language information is used to improve the results. We review different approaches for all of these stages and describe selected approaches in more detail. Furthermore, we introduce some preprocessing steps which have been developed especially for whiteboard notes. In order to assess the advantages of different methods, we present the results of a broad experimental analysis on a large database of handwritten whiteboard notes.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence