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

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Figure 20: Comparative using different features. using background pixels the results are better. Using background pixels as reference, the number of pixels that gives information to the feature vector is higher than using foreground pixels. 9.5 Discussions We have used a ground truth in this work composed by 50 documents and 20 classes, and a subset of the first one composed by 30 documents and 10 classes. As it was expected, we have observed that, by using higher number of observations from each class, the results improve. We have used two different approaches in this work. In the hierarchical approach the first layer is created by using basic features. The features that obtained given better classification performance are width and height, and the optimal number of cluster is three: small words, medium words and big words. Using this simple clustering we separate the observations in three categories. The classification process only chooses the cluster taking into account the size of the word. The second layer classes uses BSM features. The observations of each cluster of the first layer are bunched using BSM features. The results show that there is some confusion in some words, and a third layer, using other kind of feature, helps to the classification to obtain better results. The second approach has obtained the best results. This process not depends of how good was the segmentation process, is more stable that the first one, and it is showed in the results showed in this work. 28
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/ 29
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