Segmentation and recognition of on-line Pitman shorthand outlines using neural networks
This paper presents a novel approach for the segmentation and recognition of the on-line vocalized outlines of Pitman shorthand. Due to its low redundancy, the recognition of the Pitman Shorthand requires high-performance outline segmentation and stroke classification. Our approach includes (1) the segmentation of the vocalized outlines, including the detection of over-segmentation using a neural network, (2) the recognition of Pitman shorthand consonant signs using another neural network, and (3) the word recognition based on the estimation of the overall confidence on the stroke classification. Experimental results on a small test set containing 68 most frequently used English words are reported in the paper. The average accuracy on these test words can reaches 89.6% by using our approach.