International audienceRecent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology

The importance of having stable information extraction algorithms for security related applications and more generally for industrial use cases has been recently highlighted. Stability is what makes an algorithm reliable as it gives a guarantee that the results will be reproducible on similar data. Without it, security criteria such as the probability of false positives cannot be quantified. As a consequence, no security application can be built from an unstable algorithm. In a document verification framework, the probability of false positives indicates the probability that two different results are given for two copies of the same document. This paper builds on our previous work about a stable layout descriptor to study the stability of four segmentation algorithms. We consider that a segmentation algorithm is stable if it produces the same layout for all copies of the same document. The algorithms studied are two versions of PAL, Voronoi, and JSEG. We compare the stability of the different algorithms and study the factors influencing their stability.