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 18th century. The experiments show the efficiency of the detection system, and validate the relevance of the proposed contextual feature to improve detection performances.