Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition
Abstract
A complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CDVDHMM) is described. A new segmentation algorithm based on mathematical morphology is used to translate the 2-D image into a 1-D sequence of sub-character symbols. This sequence of symbols is modeled by the CDVDHMM. Generally, there are two information sources associated with the written text. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. In this context, the variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. Some experimental results are described to demonstrate the success of the proposed scheme.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Chen et al. "Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341066Markdown
[Chen et al. "Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/chen1993cvpr-variable/) doi:10.1109/CVPR.1993.341066BibTeX
@inproceedings{chen1993cvpr-variable,
title = {{Variable Duration Hidden Markov Model and Morphological Segmentation for Handwritten Word Recognition}},
author = {Chen, Mou-Yen and Kundu, Amlan and Srihari, Sargur N.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1993},
pages = {600-601},
doi = {10.1109/CVPR.1993.341066},
url = {https://mlanthology.org/cvpr/1993/chen1993cvpr-variable/}
}