ML Anthology

Off-Line Handwritten Word Recognition (HWR) Using a Single Contextual Hidden Markov Model

Mou-Yen Chen, Amlan Kundu, Jian Zhou
CVPR 1992 pp. 669-672
doi:10.1109/CVPR.1992.223205 /cvpr/1992/chen1992cvpr-off/

Abstract

A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Chen et al. "Off-Line Handwritten Word Recognition (HWR) Using a Single Contextual Hidden Markov Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223205

Markdown

[Chen et al. "Off-Line Handwritten Word Recognition (HWR) Using a Single Contextual Hidden Markov Model." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/chen1992cvpr-off/) doi:10.1109/CVPR.1992.223205

BibTeX

@inproceedings{chen1992cvpr-off,
  title     = {{Off-Line Handwritten Word Recognition (HWR) Using a Single Contextual Hidden Markov Model}},
  author    = {Chen, Mou-Yen and Kundu, Amlan and Zhou, Jian},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1992},
  pages     = {669-672},
  doi       = {10.1109/CVPR.1992.223205},
  url       = {https://mlanthology.org/cvpr/1992/chen1992cvpr-off/}
}