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

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layer uses an automatic method. It uses the Davies-Boulding index to choose the best number of clusters. Both layers uses k-means as clustering algorithm. The first layer uses direct method because we have obtained several hierarchical structures, more concretely we have compute from 3 to 30 clusters. Second layer uses an index to choose the best number of clusters, because the number of experiments is exponential as the number of clusters. Searching words The process of searching a word consists in, given a query word image, classify it regarding the different clusters. Once the most similar cluster is selected, the method “spots” the word instances in the document images. Classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics (features) inherent in the items and based in training set (learning process) of previously labelled items. To classify words we have used a k-NN approach. It is an algorithm that classifies each sample (feature extracted from the word) into one of the groups, that in the leaning process we have created, using the nearest neighbour method. Classification process is used in both levels of this work, and k-NN is used in both levels to classify our observations in the different clusters, previously established. 8. Pseudo-Structural descriptor organized in a hash structure The second approach of this work is oriented to the features. It does not matter where appear in the image. This approach uses a table of indexation to organize the observations of the experiments. We have developed a second approach which is characteristic-point centred, i.e. the indexation terms are individual features, so words are detected based on a voting process. Feature vector vary depending of the position of the words. Features used in this approach are invariant under translation of the word. There is no need to center or left-justify all the observations of the same word to obtain good results. 8.1 Feature extraction The characteristic Loci features were devised by Glucksman and applied to the classification of mixed-font alphabetic, as described in [8]. A characteristic Loci feature is composed by the number of the intersections in the four directions (up, down, right and left). For each background pixel in a binary image, and each direction, we count the number of intersections (an intersection means a black/white transition between two consecutive pixels). Then we obtain a number, that it is composed by the number of intersections in the four directions (Fig. 12). The feature vector consists in the histogram of the intersection counts. This work presents a new feature descriptor based in the characteristic Loci features. We have introduced three variations of the basic descriptor: • We have added the two diagonal directions, as we can see in figure 12. This gives more information to the feature and more sturdiness to the method. 18
• The number of the intersections is quantized.We have bounded the number of intersections in intervals. Each direction has a different interval. This bounding do more robust the feature. • Two modes are implemented to compute the feature vector, namely background and foreground pixels. To obtain the number of intersections for each direction a thinning operator is previously applied to the image. Thinning allows to get the skeleton of the image consisting of lines of width of 1 pixel. Figure 12: Characteristic Loci feature of a single point of the word page. The feature vector is computed by assigning a number to each background (or foreground) pixel as show in Fig. 12. The features are computed according to the number of intersections with the the background pixels of the image in right, upward, left and downward directions. In previous works, the characteristic Loci method has been applied for digit and isolated letter recognition. In this work, to reduce the dimension of the feature space the maximum number of intersection has been limited to 3 values (0, 1 and 2). Delimiting the number of possible values we reduce the number of combinations. The length of the feature vector is proportional to the number of possible values. For example, with 3 possible values and 8 directions, we obtain 3 8 (6561) combinations; with 4 possible values we have 3 4 (65536). It increases in exponential way and the computational cost (and time) increases in the same way. Characteristic Loci feature was designed for digit and isolated letter recognition, and the number of intersections was bounded. The original approach uses the same interval in all directions. In this work we have also bounded the number of intersections. We have normalized the number of intersections. For each direction we have defined a different interval for each value. The horizontal direction has a bigger interval than the vertical direction. In the original approach the digits or characters have a similar height and width, but in our approach the width of the words is usually bigger than the height. According with the dimensions of the words the range of the intervals are in harmonious. Diagonal directions are a combination of the two other directions. Table 1 shows the intervals for each direction. According to the above encoding, for each background pixel, an eight digit number in base 3 is obtained. For instance, the locus number of point P in Fig. 12 is (22111122)3 = (6170)10. The locus numbers are between 0 and 6561 (= 3 8 ). This is done for all background pixels. In this case, 19
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