An approach for segmentation of handwritten touching numeral strings is presented in this paper. A neural network has been designed to deal with various types of touching observed frequently in numeral strings. A numeral string image is split into a number of line segments while stroke extraction is being performed and the segments are represented with straight lines. Four types of primitive are defined based on the lines and used for representing the numeral string in more abstractive way and extracting clues on touching information from the string. Potential segmentation points are located using the neural network by active interpretation of the features collected from the primitives. Also, the run-length coding scheme is employed for efficient representation and manipulation of images. On a test set collected from real mail pieces, the segmentation accuracy of 89.1% was achieved, in image level, in a preliminary experiment.