This paper presents an architecture well suited for natu- ral images classification or visual object recognition applications. The method proposes to integrate a spatial representation into the well known ”bag of local signatures” approach. For this purpose, it combines the power of a string representation which provides an ordered view of local features with the vectorial histogram representation allowing to recognize efficiently and quickly an image by using a machine learning classifier. To reach this goal, we propose to represent an image by a set of strings of local signatures obtained by tracking the detected salient points along image edges. We propose here to conjointly use the Holder exponents and the direction of minimal regularity of the bidimensionnal signal sin- gularities to compute a signature describing precisely a region of interest centered on an interest point. As we will see, an alphabet of strings is easily obtained by using a typical self organizing map architecture. As a consequence, a ”bag of strings” representation is used, providing a compact representation encoding both local signatures and spatial infor- mation. This representation is particularly well suited to train a support vector machine classifier used for the last classification step. This archi- tecture obtains good classification rates on different well known datasets.