  Word clouds and text visualization is one of the recent most popular and
widely used types of visualizations. Despite the attractiveness and simplicity
of producing word clouds, they do not provide a thorough visualization for the
distribution of the underlying data. Therefore, it is important to redesign
word clouds for improving their design choices and to be able to do further
statistical analysis on data. In this paper we have proposed a fully automatic
redesigning algorithm for word cloud visualization. Our proposed method is able
to decode an input word cloud visualization and provides the raw data in the
form of a list of (word, value) pairs. To the best of our knowledge our work is
the first attempt to extract raw data from word cloud visualization. We have
tested our proposed method both qualitatively and quantitatively. The results
of our experiments show that our algorithm is able to extract the words and
their weights effectively with considerable low error rate.
