Europol and the EU’s fight against organized crime: exploring the potential of experimentalist governance

While issues relating to the development, legitimacy and accountability of the European Police Office, Europol, have been intensively discussed in political and academic circles, the actual impact of Europol on policy-making in the European Union has yet to receive scholarly attention. By investigating the evolution and the role of Europol's organized crime reports, this article elaborates on whether Europol has been able to exert an influence beyond its narrowly defined mandate. Theoretically informed by the assumptions of experimentalist governance, the article argues that the different legal systems and policing traditions of EU member states have made it difficult for the EU to agree on a common understanding on how to fight against organized crime. This lack of consensus, which has translated into a set of vague and broadly formulated framework goals and guidelines, has enabled Europol to position its Organized Crime Threat Assessments as the point of reference in the respective EU policy-making area. Europol's interest in improving its institutional standing thereby converged with the interest of different member states to use Europol as a socialization platform to broadcast their ideas and to ‘Europeanize’ their national counter-organized crime policy.

Handwritten and machine-printed text discrimination using a template matching approach

We propose a novel template matching approach for the discrimination of handwritten and machine-printed text. We first pre-process the scanned document images by performing denoising, circles/lines exclusion and word-block level segmentation. We then align and match characters in a flexible sized gallery with the segmented regions, using parallelised normalised cross-correlation. The experimental results over the Pattern Recognition & Image Analysis Research Lab-Natural History Museum (PRImA-NHM) dataset show remarkably high robustness of the algorithm in classifying cluttered, occluded and noisy samples, in addition to those with significant high missing data. The algorithm, which gives 84.0% classification rate with false positive rate 0.16 over the dataset, does not require training samples and generates compelling results as opposed to the training-based approaches, which have used the same benchmark.