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

The segmentation process experiments evaluate the accuracy of the word segmentation. The
segmented word and the labeled word are overlapped in order to check if they are the same word.
Different thresholds of overlapping percentage are used to evaluate the accuracy of the segmentation
process.
The first approach has two types of experiments. The first one evaluates how the clustering
process is done. The second one evaluates the accuracy of the retrieval process:
• The experiment shows the relation between the basic features chosen by using 2D plots.
• By using visual results, we observe the distribution of the observations of our ground truth
in the clusters.
• We evaluate the accuracy, the homogeneity and completeness of the clustering using Vmeasure
(explained in section 9.3).
• The accuracy of the retrieval process is evaluated by means of a precision-recall curve.
The second approach is evaluated by means of precision-recall curves:
• Two experiments are used to assess the accuracy of this approach by using different characteristics
pixels (background and foreground pixels).
• Both characteristics points are compared in order to evaluated.
9.3 Metrics
One drawback of clustering process is the proper selection of the number of clusters. Learning
process consist in bunching the observations in different clusters. The ideal solution is achieved
when all the instances of the same word are in the same cluster, and each cluster has only instances
of only one word. The results of the retrieval process depend on the accuracy in the clustering process.
The evaluation of the clustering process has been done using V-measure [22]. V-measure is an
entropy-based measure which explicitly measures how successfully the criteria of homogeneity and
completeness have been satisfied. V-measure is computed as the “mean” of distinct homogeneity
and completeness scores, that is, V-measure can be weighted to favour the contributions of homogeneity
or completeness. A clustering result satisfies homogeneity if each one of its clusters contain
only data points which are members of a single class, and a clustering result satisfies completeness
if all the data points that are members of a given class are elements of the same cluster
The retrieval process is evaluated using precision-recall curves:
recall =
precision =
number of relevant items retrieved
number of relevant items in collection
number of relevant items retrieved
total number of items retrieved
22
(4)
(5)