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

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Figure 22: Classification process using different basic features and BSM features. (a) 10 pixels (b) 100 pixels Figure 23: Examples with different mask sizes. terms, making it possible to use costly human labor more sparingly than a full transcription would require. In this work we have introduced two approaches to do word spotting. Both approaches use predefined descriptors and the observations are stored in different structures. Our first approach is based in pixel-based descriptors organized in a hierarchical structure. Our second approach is based on a pseudo-structure descriptor organized in a hash structure. Experimental results of the first approach show that the selected basic features cannot discriminate enough the observations. The results are very confused but they can be used in order to do a fast rejection of, for example, small words, medium words and big words. 30
(a) Foreground pixels as reference (b) Background pixels as reference Figure 24: Different mask sizes using different characteristics pixels as reference. Figure 25: Comparative results using background and foreground pixels as reference. In order to compare the descriptors used in the first approach (basic and BSM features), we conclude that BSM features work better than basic features using k-means as clustering algorithm. Concerning the results of the second approach, we have obtained results using the background and the foreground as characteristic pixels. In both cases the performance results are similar. But if we compare them, we conclude that by using background pixels the results are better than when we 31
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Page 10 and 11: The models can then be used to retr
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Page 14 and 15: 6.1.1 Binarization The binarization
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Handwritten Word Spotting in Old Manuscript Images using Shape ...

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