International audienceMulti-scale aggregates are composed of particles which results themselves of agglomeration of other primary particles. If particles are modeled by their centers, the geometrical characterization of aggregates refers to point pattern analysis. Radial distribution and function of pairs allow a description of the point pattern to be performed. They describe how points are radially packed around each other. In this paper, the characterization of different simulated aggregates are computed and compared
Digitized re-publishing of documents has become nowadays a very important issue. Optical Character Recognition (OCR) has been intensively used to this aim, as it performs the transcription of the text images into electronic files, allowing display functionalities, indexation, enrichment and broadcasting. However, such software still fails in many configurations, so that the transcription does not reach the required editorial quality (99% of recognition are required for an ergonomic reading). In the OZALID project, we propose to rely on crowdsourcing for correcting OCR results. One main issue is then to determine when the crowdsourcing has reached its limits. For that, we present a feasibility study of an original protocol based on indicators that quantify the recognition quality in both semantic and semiotic ways. These indicators are calculated and followed up during the entire crowdsourcing process until stability. Experimental results show that the proposed observables converge after some correction iterations allowing automatically stopping the crowdsourcing process and dealing with huge amount of data.
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