This paper deals with the difficult problem of indexing ancient graphic images. It tackles the particular case of indexing dropcaps (also called Lettrines), and specifically considers the problem of letter extraction from this complex graphic images. Based on an analysis of the features of the images to be indexed, an original strategy is proposed. This approach relies on filtering the relevant information, on the basis of Meyer decomposition. Then, in order to accommodate the variability of representation of the information, a Zipf law modeling enables detection of the regions belonging to the letter, what allows it to be segmented. The overall process is evaluated using a relevant set of images, which shows the relevance of the approach.
This paper presents a new method to extract shapes in drop caps and particularly the most important shape: Letter itself. This method relies on a combination of a Aujol and Chambolle algorithm threaded with a segmentation using a Zipf Law in a second step. This method can be enhanced as a three-step process: 1)Decomposition in layers 2)Segmentation using a Zipf Law 3)Selection of connected components to only emphasize the required informationletter itself.
Ancient graphical documents are invaluable heritages which have been handed down since generations. They possess both intellectual and spiritual worth for humanity. In this context, many digitization processes have been started, producing very large warehouse of images. These huge amount of data raise the problem of indexing the information in order to make easier navigation in the databases. In the context of a French research program, called MADONNE, this paper proposes a set of complementary contributions concerning ancient graphic images indexing.
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