Segmentation and recognition of handwritten Pitman shorthand outlines using neural networks

An approach for segmentation and recognition of on-line vocalized outlines of Pitman shorthand is presented. The approach is to use a trained neural network for the segmentation of the vocalized outlines for the detection of over-segmentation; and to use another trained neural network for the recognition of Pitman shorthand consonant signs; while the word recognition was based on the estimation of the overall confidence on the stroke classification. Experimental results on a test set containing 68 most frequently used English words showed that on average, the approach can achieve an accuracy rate of 89.6%.