The segmentation of cursive and mixed scripts persists to be a difficult problem in the area of handwriting recognition. This research details advances for segmenting characters in off-line cursive script. Specifically, a heuristic algorithm and a neural network-based technique, which uses a structural feature vector representation, are proposed and combined for identifying incorrect segmentation points. Following the location of appropriate anchorage points, a character extraction technique, using segmentation paths, is employed to complete the segmentation process. Results are presented for neural-based heuristic segmentation, segmentation point validation, character recognition, segmentation path detection and overall segmentation accuracy.