Handwriting recognition of whiteboard notes - Studying the influence of training set size and type

This paper presents a system for the recognition of online 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 offline HMM-recognizer, which is supplemented with methods for processing the online data and generating offline images. The system consists of six main modules: online preprocessing, transformation of online to offline data, offline preprocessing, feature extraction, classiffication and post-processing. The recognition rate of our 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%. To further improve the system performance we increased the size of the training set. For that we investigated two different strategies. First, we used another existing database of offline handwritten text. Second, we used a recently collected whiteboard database, called the IAM-OnDB. By means of these strategies the recognition rate could be further increased up to 68.5%.