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

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7. Pixel-based descriptors organized in a hierarchical structure The first approach of this work is based in two pixel-based descriptors (basic features and BSM features), and they are organized using a hierarchical structure of clusters. The objective is to do several layers of clusters using diverse features. Top layer is composed by basic features. Down layer consists of pixel distribution based features, in particular BSM. This approach bunches the words into clusters with similar features. When we downward of layer, we only use the observations of the cluster chosen to cluster the words with the new kind of features (Fig. 10). We reduce in each layer the number of observations that we are computing features for the new layer, and the classification process is faster. 7.1 Feature extraction In pattern recognition and in image processing feature extraction is a special form of dimensionality reduction. The objective is to transform the input data into a reduced representation set of features (feature vector). The observations of the experiments have different ways to represent those using different features. The objective is to select the best features that describe the image. The marriage licences corpus of the cathedral of Barcelona is composed of 244 volumes, too much information to be indexed directly. Consequently the computational time cost increase with the number of documents of our corpus. Retrieval time can be reduced using a hierarchical indexation structure. The features of our corpus are divided in several groups (clusters) in each layer, and then, each layer is divided in other groups using other features (Fig. 10). Figure 10: The structure in layers of the feature extraction. In this work two types of features have been used: Basic features and BSM features. The first layer uses basic features to do a rough separation of the word classes. In the second layer we have used features based in pixel distribution. Each cluster of the first layer is split using BSM features. 16
Basic features Basic features are based in shape features of the images [28]. These features are extracted from the contour and the region of the shapes. For each normalized word, a sequence of basic feature vectors is obtained. The features used in this work are: height, width, aspect-ratio, centroid, filled-area, perimeter, eccentricity, Euler Number. The objective of this first layer is to separate all the words of our corpus in groups, with similar basic features. Blurred Shape Model (BSM) features The words are described by a probability density function of Blurred Shape Model (BSM) [5] that encodes the probability of pixel densities of image regions: The image is divided in a grid of n x n equal-sized subregions, and each bin receives votes from the shape points in it and also from the shape points in the neighbouring bins. Thus, each shape point contributes to a density measure of its bin and its neighbouring ones. The output descriptor is a vector histogram where each position corresponds to the density in the context of the sub-region (Fig. 11). The objective of this second layer is to extract features based in pixel distributions. Once the words have been clustered according the size, this layer matches the words with similar pixel distribution in different clusters. (a) Original image (b) Shape pixel distances estimation respect to neighbour centroids 7.2 Learning and retrieval Organizing features (c) 16 regions blurred shape Figure 11: Blurred Shape Model (BSM). Extracted from [6] The goal of a learning stage correspond to acquire new knowledge, behaviours, skills, values, preferences or understanding, and may involve synthesizing different types of information. The learning process of this work consists in extracting features of the words, calculating the distance between them, and bunching them with respect the distance (called clustering process). The clustering processes have a drawback: to know what is the best number of clusters which the observations of our experiments are better bunched. This approach has a hierarchical structure of clusters. Each layer of the structure has to be clustering using one of the methods explained in the section 4. The first layer uses the direct method to choose the number of clusters. The second 17
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Handwritten Word Spotting in Old Manuscript Images using Shape ...

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