This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, nonrigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.

This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multi-levels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multi-dimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the "a priori" number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a non-traditional visualization approach based on a multi-agents system which combines both dimension reduction and interactivity.

This paper describes the ICDAR2017 competition on post-OCR text correction and presents the different methods submitted by the participants. OCR has been an active research field for over the past 30 years but results are still imperfect, especially for historical documents. The purpose of this competition is to compare and evaluate automatic approaches for correcting (denoising) OCR-ed texts. The challenge consists of two independent tasks: 1) error detection and 2) error correction. An original dataset of 12M OCR-ed symbols along with an aligned ground truth was provided to the participants with 80% of the dataset dedicated to the training and 20% to the evaluation. Different sources were aggregated and namely contain newspapers and monographs covering 2 languages (English and French). 11 teams submitted results, while the difficulty of the task was underlined by the fact that only half of the submitted methods were able to denoise the evaluation dataset on average. In any case, this competition, which counted 35 registrations, illustrates the strong interest of the community in this essential problem, which is key to any digitization process involving textual data.

In video-surveillance, non-parametric Bayesian approaches based on a Hierarchical Dirichlet Process (HDP) have recently shown their efficiency for modeling crowed scene activities. This paper follows this track by proposing a method for detecting and clustering emergent behaviors across different captures made of numerous unconstrained trajectories. Most HDP applications for crowed scenes (e.g. traffic, pedestrians) are based on flow motion features. In contrast, we propose to tackle the problem by using full individual trajectories. Furthermore, our proposed approach relies on a three-level clustering hierarchical Dirichlet process able with a minimum a priori to hierarchically retrieve behaviors at increasing semantical levels: activity atoms, activities and behaviors. We chose to validate our approach on ant trajectories simulated by a Multi-Agent System (MAS) using an ant colony foraging model. The experimentation results have shown the ability of our approach to discover different emergent behaviors at different scales, which could be associated to observable events such as "forging" or "deploying" for instance.