Recognition-Based Segmentation of On-Line Hand-Printed Words
Abstract
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.
Cite
Text
Schenkel et al. "Recognition-Based Segmentation of On-Line Hand-Printed Words." Neural Information Processing Systems, 1992.Markdown
[Schenkel et al. "Recognition-Based Segmentation of On-Line Hand-Printed Words." Neural Information Processing Systems, 1992.](https://mlanthology.org/neurips/1992/schenkel1992neurips-recognitionbased/)BibTeX
@inproceedings{schenkel1992neurips-recognitionbased,
title = {{Recognition-Based Segmentation of On-Line Hand-Printed Words}},
author = {Schenkel, M. and Weissman, H. and Guyon, I. and Nohl, C. and Henderson, D.},
booktitle = {Neural Information Processing Systems},
year = {1992},
pages = {723-730},
url = {https://mlanthology.org/neurips/1992/schenkel1992neurips-recognitionbased/}
}