Enhancing training data for handwriting recognition of whiteboard notes with samples from a different database
Marcus Liwicki, Horst Bunke · 2005
Recognition of unconstrained handwritten text is still a challenge. In this paper we consider a new problem, which is the recognition of notes written on a whiteboard. Our recognizer is based on hidden Markov models (HMMs). As it is difficult to acquire sufficient amounts of training data for the HMMs we propose two strategies for enlarging the training set. Both strategies are based on an existing database of offline handwritten text, which includes handwriting samples different from whiteboard data. The two proposed strategies are MAP adaptation and merging of training sets. With these methods we can achieve improvements of the word recognition rate of up to 5.7%.