PDF became a very common format for exchanging printable documents. Further, it can be easily generated from the major documents formats, which make a huge number of PDF documents available over the net. However its use is limited to displaying and printing, which considerably reduces the search and retrieval capabilities. For this reason, additional tools have recently appeared that allow to extract the textual content. However their practical use is limited in the sense that the text's reading order is not necessary preserved, especially when handling multi-column documents, or in presence of complex layout. Our thesis is that those tools do not consider the hidden layout and logical structures of documents, which could greatly improve their results.We propose a novel approach to overcome the document content extraction, by merging a) low-level extraction methods applied on PDF files with b) layout analysis performed on a synthetically generated TIFF image. The paper describes the various steps necessary to achieve this task. Finally, we present a first experiment on the restitution of the newspapers' reading order which shows encouraging results.

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The aim of layout analysis is to extract the geometric structure from a document image. It consists of labeling homogenous regions of a document image. This paper describes the performance of segmentation algorithms and their adaptation in order to treat complex structured Arabic documents such as newspapers. Experimental tests have been carried out on four different phases of newspaper image analysis: thread recognition, frame recognition, image text separation, text line recognition, and line merging into blocks. Some promising experimental results are reported.

Accessing the structured content of PDF document is a difficult task, requiring pre-processing and reverse engineering techniques. In this paper, we first present different methods to accomplish this task, which are based either on document image analysis, or on electronic content extraction. Then, XCDF, a canonical format with well-defined properties is proposed as a suitable solution for representing structured electronic documents and as an entry point for further researches and works. The system and methods used for reverse engineering PDF document into this canonical format are also presented. We finally present current applications of this work into various domains, spacing from data mining to multimedia navigation, and consistently benefiting from our canonical format in order to access PDF document content and structures.

Nowadays any intrusion detection system should include decision making feature. Each network administrator, in his everyday job, is overwhelmed with a big number of events and alerts. It is a challenge to be able to take correct decisions and to classify events according to their accuracy. That’s why we need to provide the administrator with the right tools in order to help him taking the correct decision. For this purpose, we suggest an Artificial Neural Networks (ANN) architecture for decision making within intrusion detection systems. Having in mind our IMA IDS solution that presents a global agent architecture for enhanced intrusion network based solution, we are including ANN as a major decision algorithm using the learning and adaptive features of ANN. This inclusion aims to increase respectively efficiency, by reducing the fault positive, and detection capabilities by allowing detection with partial available information on the network status.