ML Anthology

Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data

Michael C. Burl, Pietro Perona
CVPR 1998 pp. 535-540
doi:10.1109/CVPR.1998.698657 /cvpr/1998/burl1998cvpr-using/

Abstract

Different instances of a handwritten word consist of the same basic features (humps, cusps, crossings, etc.) arranged in a deformable spatial pattern. Thus, keywords in cursive text can be detected by looking for the appropriate features in the "correct" spatial configuration. A keyword can be modeled hierarchically as a set of word fragments, each of which consists of lower-level features. To allow flexibility, the spatial configuration of keypoints within a fragment is modeled using a Dryden-Mardia (DM) probability density over the shape of the configuration. In a writer-dependent test on a transcription of the Declaration of Independence (/spl sim/1300 words, /spl sim/7500 characters), the method detected all eleven instances of the keyword "government" with only four false positives.

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Cite

Text

Burl and Perona. "Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998. doi:10.1109/CVPR.1998.698657

Markdown

[Burl and Perona. "Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1998.](https://mlanthology.org/cvpr/1998/burl1998cvpr-using/) doi:10.1109/CVPR.1998.698657

BibTeX

@inproceedings{burl1998cvpr-using,
  title     = {{Using Hierarchical Shape Models to Spot Keywords in Cursive Handwriting Data}},
  author    = {Burl, Michael C. and Perona, Pietro},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1998},
  pages     = {535-540},
  doi       = {10.1109/CVPR.1998.698657},
  url       = {https://mlanthology.org/cvpr/1998/burl1998cvpr-using/}
}