Information extraction from printed documents is still a crucial problem in many interorganizational workflows. Solutions for other application domains, for example, the web, do not fit this peculiar scenario well, as printed documents do not carry any explicit structural or syntactical description. Moreover, printed documents usually lack any explicit indication about their source. We present a system, which we call PATO, for extracting predefined items from printed documents in a dynamic multisource scenario. PATO selects the source-specific wrapper required by each document, determines whether no suitable wrapper exists, and generates one when necessary. PATO assumes that the need for new source-specific wrappers is a part of normal system operation: new wrappers are generated online based on a few point-and-click operations performed by a human operator on a GUI. The role of operators is an integral part of the design and PATO may be configured to accommodate a broad range of automation levels. We show that PATO exhibits very good performance on a challenging data set composed of more than 600 printed documents drawn from three different application domains: invoices, datasheets of electronic components, and patents. We also perform an extensive analysis of the crucial tradeoff between accuracy and automation level.

Internet security threats often involve the fraudulent modification of a web site, often with the addition of new pages at URLs where no page should exist. Detecting the existence of such hidden URLs is very difficult because they do not appear during normal navigation and usually are not indexed by search engines. Most importantly, drive-by attacks leading users to hidden URLs, for example for phishing credentials, may fool even tech-savvy users, because such hidden URLs are increasingly hosted within trusted sites, thereby rendering HTTPS authentication ineffective. In this work, we propose an approach for detecting such URLs based only on their lexical features, which allows alerting the user before actually fetching the page. We assess our proposal on a dataset composed of thousands of URLs, with promising results.

A key step in the understanding of printed documents is their classification based on the nature of information they contain and their layout. In this work we consider a dynamic scenario in which document classes are not known a priori and new classes can appear at any time. This open world setting is both realistic and highly challenging. We use an SVM-based classifier based only on image-level features and use a nearest-neighbor approach for detecting new classes. We assess our proposal on a real-world dataset composed of 562 invoices belonging to 68 different classes. These documents were digitalized after being handled by a corporate environment, thus they are quite noisy---e.g., big stamps and handwritten signatures at unfortunate positions and alike. The experimental results are highly promising.