Unsupervised Font Clustering Using Stochastic Versio of the EM Algorithm and Global Texture Analysis

@inproceedings{AvilsCruz2004UnsupervisedFC,
  title={Unsupervised Font Clustering Using Stochastic Versio of the EM Algorithm and Global Texture Analysis},
  author={Carlos Avil{\'e}s-Cruz and Juan Villegas-Cortez and Ren{\'e} Arechiga-Mart{\'i}nez and Rafael Escarela-Perez},
  booktitle={CIARP},
  year={2004}
}
An Unsupervised Font clustering technique is proposed in this work. The new approach is based on global texture analysis, using high order statistic features, Gaussian classifier and a stochastic version of the EM algorithm. The font recognition is performed by taking the document as a simple image, where one or several types of fonts are present. The identification is not performed letter by letter as with conventional approaches. In the proposed method a window analysis is employed to obtain… CONTINUE READING
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Key Quantitative Results

  • The font recognition with clean images is 100% accurate.

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