Characterizing noisy or ancient documents is a challenging problem up to now. Many techniques have been done in order to effectuate feature extraction and image indexation for such documents. Global approaches are in general less robust and exact than local approaches. That's why, we propose in this paper, a hybrid system based on global approach (fractal dimension), and a local one, based on SIFT descriptor. The Scale Invariant Feature Transform seems to do well with our application since it is rotation invariant and relatively robust to changing illumination. In the first step the calculation of fractal dimension is applied to images, in order to eliminate images which have distant features than image request characteristics. Next, the SIFT is applied to show which images match well the request. However, the average matching time using the hybrid approach is better than "fractal dimension" and "SIFT descriptor" techniques, if they are used alone.
In this paper we characterize Arabic and Latin ancient document images. The main criticism of existing works is that most of them are interested in the characterization of Latin historical documents, and they are up to now no many methods that can perform the discrimination between these different language old document images. Regions of images having the same size (256*256 pixels) were extracted from our heterogeneous base. Fractal dimension method is used to discriminate between ancient Arabic and Latin scripts. We achieve 95.87% accuracy on the discrimination between Arabic and Latin ancient document collections. The main advantage of our approach is that it can be easily adapted for the identification of other ancient document collections and we can have better recognition rates by adding relative features of each document base. The encouraging and promising results lead us to study the retrieval of ancient images based on the same technique in order to retrieve similar collections to the image request.
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