ML Anthology

Indirect Symbolic Correlation Approach to Unsegmented Text Recognition

George Nagy, Sharad C. Seth, Shashank K. Mehta, Yu Lin
CVPRW 2003 pp. 22
doi:10.1109/CVPRW.2003.10028 /cvprw/2003/nagy2003cvprw-indirect/

Abstract

The new non-parametric approach to unsegmented text recognition builds two bipartite graphs that result from the feature-level and lexical comparisons of the same word against a reference string which need not include the query word. The lexical graph preserves the relative order of edges in the feature graph corresponding to correctly recognized features. This observation leads to a subgraph-matching formulation of the recognition problem. An initial implementation proves the robustness of the approach for up-to 20% noise introduced in the feature-level graph.

PDF CVPRW Semantic Scholar

Cite

Text

Nagy et al. "Indirect Symbolic Correlation Approach to Unsegmented Text Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10028

Markdown

[Nagy et al. "Indirect Symbolic Correlation Approach to Unsegmented Text Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/nagy2003cvprw-indirect/) doi:10.1109/CVPRW.2003.10028

BibTeX

@inproceedings{nagy2003cvprw-indirect,
  title     = {{Indirect Symbolic Correlation Approach to Unsegmented Text Recognition}},
  author    = {Nagy, George and Seth, Sharad C. and Mehta, Shashank K. and Lin, Yu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2003},
  pages     = {22},
  doi       = {10.1109/CVPRW.2003.10028},
  url       = {https://mlanthology.org/cvprw/2003/nagy2003cvprw-indirect/}
}