Contextual detection of drawn symbols in old maps

In this paper, we tackle the problem of detecting drawn symbols in old maps. We propose a novel approach that combines powerful low level descriptors to represent the local content of the objects, and contextual features to overcome the local analysis ambiguity. Our contribution is two-fold. Firstly, we propose a novel contextual feature adapted to our problem, where the context is integrated at two levels. In a close neighborhood, a local analysis is carried out to remove visual ambiguities between symbols. In a larger extent, co-occurrence statistics between classes are stored. Secondly, we propose an entire processing chain for learning and detection. The proposed method is evaluated on real french maps from the 18^th century. The experiments show the efficiency of the detection system, and validate the relevance of the proposed contextual feature to improve detection performances.