A soft-decision approach for symbol segmentation within handwritten mathematical expressions

A soft-decision approach for symbol segmentation within on-line sampled handwritten mathematical expressions is presented. Based on stroke-specific features as well as geometrical features between the strokes a symbol hypotheses net is generated. For assistance additional knowledge obtained by a symbol prerecognition stage is used. The results achieved by the segmentation and prerecognition experiments indicate the performance of our approach.