RECOGNITION-BASED SEGMENTATION OF ONLINE RUN-ON HANDPRINTED WORDS - INPUT VS OUTPUT SEGMENTATION

The performance of two methods for recognition-based segmentation of strings of on-line handprinted capital Latin characters is reported. The input strings consist of a time-ordered sequence of X, Y coordinates, punctuated by pen-lifts. The methods are 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 different. The first method, which we call INSEG (for input segmentation), uses a combination of heuristics to identify particular pen-lifts as tentative segmentation points. The second method, which we call OUTSEG (for output segmentation), relies on the empirically trained recognition engine for both recognizing characters and identifying relevant segmentation points. The best results are obtained with the INSEG method: 11% error on handprinted words from an 80,000 word dictionary.