Handwritten Word Spotting in Old Manuscript Images using Shape ...

Figure 21: Choosing the best number of clusters. β = 0 means homogeneity. β = 1 means
completeness.
We have observed that the clustering algorithm used in the first approach does not perform
well with the selected corpus. We have done some experiments with Self-Organizing Map 1 (SOM)
as an introductory work for future endeavours. SOM is a type of Artificial Neural Network that
is trained using unsupervised learning to produce a low-dimensional, discrete representation of the
input space of the training examples, called a map.In the appendix A there are some figures with
the results of this algorithm. Figure 26 shows a map of the observations of the training set using
BSM features. Each cell represents a different cluster, and each colour a different class. We observe
that the observations of each class are bunched in close clusters. Figure 27 shows a similar to the
previous one, but using characteristic Loci as features. We observe that in this case the observations
are more concentrated in the same clusters.
10. Conclusions
Word-spotting appears to be an attractive alternative to the seemingly obvious recognize-thenretrieve
approach to historical manuscript retrieval. With the capability of matching word images
in a quick and accurate way, partial transcriptions of a collection can be achieved with reasonable
accuracy and scarce human interaction and we obtain better results and by increasing the number of
observations of the training set. Word-spotting has the capability to automatically identify indexing
1. http://www.cis.hut.fi/somtoolbox/
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