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

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partial Gaussian derivatives along the two orientations at different scales, to merge the components of a word. An anisotropic Gaussian filter (Fig. 8) is defined as: 1 G(x, y; σx, σy) = e 2πσxσy x2 −( σ2 + x y2 σ2 ) y From the filter (1) the Laplacian of Gaussian operator is based on the addition of the second derivatives in x and y as follows: L(x, y; σx, σy) = Gxx(x, y; σx, σy) + Gyy(x, y; σx, σy) (2) A scale space representation of the line images is constructed by convolving the image with L from (2). Consider a two-dimensional image f(x,y); then, the corresponding output image is I(x, y; σx, σy) = L(x, y; σx, σy) ∗ f(x, y) (3) As we can see in figure 8 the output is a grey-scale image, where the background has a middle grey-level and the words are lightly grey. It is very difficult to determine a threshold for selecting the pixels that corresponds to words. We have observed that most words have black contour. Our improvement allows, using this mask, to split each word in three areas: background, word and contours of the word. The mask converts the black thin contours in thick contours. The rest of the image is considered background. This gained of the contour cause the joining of the letters that are together. This improvement allows to merge the characters of the word and is easier to split different words. The words, which are extracted from a scale space representation, are blob-like, but, to make sure that the blob merges all the parts of the words, we apply a closing operator to each word. 6.2 Fast rejection The previous process produces one blob for each word in the document, but sometimes these components do not represent words, because they are stains, lines or small parts of a word that has not been merged with the original word. The selection of the suitable words are done in two steps. First, the blobs which are very small, regarding to the height and the width of the segmented line, are rejected. For the remaining blobs, we choose those blobs with more pixels than a threshold, experimentally set. 6.3 Noise removal The images remaining after the fast rejection step are subject to a normalization process to reduce their variability. Our proposal allows to clean the image and to fit the bounding box to the word (Fig. 9). The first step consists in binarizing the word image (Fig. 9b). Then, we apply the anisotropic Gaussian filter explained before to merge the different parts of the same word (Fig. 9c). Once applied, the image is composed by several blobs, as we can see in figure 9d, then, the next step 14 (1)
Figure 8: Anisotropic Gaussian Filter is deleting the blobs that do not belong to the word. The biggest blob is chosen and its contour computed (Fig. 9e). The contour is the frontier that separates the pixels of the word and the background. The last step consist in to project in vertical and in horizontal to fix the bounding box. (a) Original Image (b) Binarized image (c) Anisotropic Gaussian filter (d) Biggest blob (e) Blob contour (f) Final image Figure 9: Normalization process 15
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