In this work we propose two novel vector quantization (VQ) designs for discrete HMM-based on-line handwriting recognition of whiteboard notes. Both VQ designs represent the binary pressure information without any loss. The new designs are necessary because standard k-means VQ systems cannot quantize this binary feature adequately, as is shown in this paper. Our experiments show that the new systems provide a relative improvement of r = 1.8% in recognition accuracy on a character- and r = 3.3 % on a word-level benchmark compared to a standard fc-means VQ system. Additionally, our system is compared and proven to be competitive to a state-of-the-art continuous HMM-based system yielding a slight relative improvement of r = 0.6%.
展开▼
