Handwriting Recognition of Whiteboard Notes

This paper introduces a new system for processing on-line whiteboard notes. Notes written on a whiteboard is a new modality in handwriting recognition research that has received relatively little attention in the past. For the recognition we use an off-line HMM-recognizer, which has been developed in the context of our previous work. The recognizer is supplemented with methods for processing the on-line data and generating the images. The system consists of six main modules: on-line preprocessing, transformation to off-line data, off-line preprocessing, feature extraction, classification and post-processing. The recognition rate of the basic recognizer in a writer independent experiment is 59,5%. By applying state-of-the-art methods, such as optimizing the number of states and Gaussian components, and by including a language model we could achieve a statistically significant increase of the recognition rate to 64.3%.