Recognition-based Segmentation of On-Line Hand-printed Words

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

Bibtex Metadata Paper


M. Schenkel, H. Weissman, I. Guyon, C. Nohl, D. Henderson


This paper reports on the performance of two methods for recognition-based segmentation of strings of on-line hand-printed capital Latin characters. The input strings consist of a time(cid:173) ordered sequence of X-Y coordinates, punctuated by pen-lifts. The methods were designed to work in "run-on mode" where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite differ(cid:173) ent. The first method, which we call IN SEC (for input segmen(cid:173) tation), uses a combination of heuristics to identify particular pen(cid:173) lifts as tentative segmentation points. The second method, which we call OUTSEC (for output segmentation), relies on the empiri(cid:173) cally trained recognition engine for both recognizing characters and identifying relevant segmentation points.