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Recognition-based Segmentation of On-line Run-on Handprinted Words: Input vs. Output Segmentation. (1992)
| Content Provider | CiteSeerX |
|---|---|
| Author | Weissman, H. Schenkel, M. Guyon, I. Nohl, C. Henderson, D. Eth-Zfirich, Also |
| Abstract | This paper reports on performance of two methods for recognition-based segmentation of strings of on-line handprinted capital Latin characters. The input strings consist of a time-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 segmenta- tion 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 sec- ond method, which we call OUTSEW (for output segmentation), relies on the empirically trained recognition engine for both recognizing characters and identifying relevant segmentation points. Our best results are obtained with the INSEG method: 11% error on handprinted words from an 80,000 word dictionary. |
| File Format | |
| Volume Number | 27 |
| Journal | Pattern Recognition |
| Language | English |
| Publisher Date | 1992-01-01 |
| Access Restriction | Open |
| Subject Keyword | Output Segmentation Recognition-based Segmentation Input V On-line Run-on Handprinted Word First Method Word Dictionary X-y Coordinate Neural Network Recognition Engine Input Segmentation Handprinted Word Relevant Segmentation Point Time-ordered Sequence Particular Pen-lifts On-line Handprinted Capital Latin Character Tentative Segmentation Point Inseg Method Input String Consist Trained Recognition Engine Sec Ond Method Graph-algorithmic Post-processor Run-on Mode |
| Content Type | Text |
| Resource Type | Article |
