At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phúc Xá district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network.

State-of-the-art methods for handwriting text recognition are based on deep learning approaches and language modeling that require large data sets during training. In practice, there are some applications where the system processes mono-writer documents, and would thus benefit from being trained on examples from that writer. However, this is not common to have numerous examples coming from just one writer. In this paper, we propose an approach to adapt both the optical model and the language model to a particular writer, from a generic system trained on large data sets with a variety of examples. We show the benefits of the optical and language model writer adaptation. Our approach reaches competitive results on the READ 2018 data set, which is dedicated to model adaptation to particular writers.

We introduce a novel handwriting recognition approach based on sub-lexical units known as multigrams of characters, that are variable lengths characters sequences. A Hidden Semi Markov model is used to model the multigrams occurrences within the target language corpus. Decoding the training language corpus with this model provides an optimized multigram lexicon of reduced size with high coverage rate of OOV compared to the traditional word modeling approach. The handwriting recognition system is composed of two components: the optical model and the statistical n-grams of multigrams language model. The two models are combined together during the recognition process using a decoding technique based on Weighted Finite State Transducers (WFST). We experiment the approach on two Latin language datasets (the French RIMES and English IAM datasets) and we show that it outperforms words and character models language models for high Out Of Vocabulary (OOV) words rates, and that it performs similarly to these traditional models for low OOV rates, with the advantage of a reduced complexity.

We propose a system designed to spot either words or patterns, based on a user made query. Employing a two stage approach, it takes advantage of the descriptive power of the Bag of Visual Words (BOVW) representation and the discriminative power of the proposed Longest Weighted Profile (LWP) algorithm. First, we try to identify the zones of images that share common characteristics with the query as summed up in a BOVW. Then, we filter these zones using the LWP introducing spatial constraints extracted from the query. We have validated our system on the George Washington handwritten document database for word spotting, and medieval manuscripts from the DocExplore project for pattern spotting.

Newspapers are documents made of news item and informative articles. They are not meant to be read iteratively: the reader can pick his items in any order he fancies. Ignoring this structural property, most digitized newspaper archives only offer access by issue or at best by page to their content. We have built a digitization workflow that automatically extracts newspaper articles from images, which allows indexing and retrieval of information at the article level. Our back-end system extracts the logical structure of the page to produce the informative units: the articles. Each image is labelled at the pixel level, through a machine learning based method, then the page logical structure is constructed up from there by the detection of structuring entities such as horizontal and vertical separators, titles and text lines. This logical structure is stored in a METS wrapper associated to the ALTO file produced by the system including the OCRed text. Our front-end system provides a web high definition visualisation of images, textual indexing and retrieval facilities, searching and reading at the article level. Articles transcriptions can be collaboratively corrected, which as a consequence allows for better indexing. We are currently testing our system on the archives of the Journal de Rouen, one of France eldest local newspaper. These 250 years of publication amount to 300 000 pages of very variable image quality and layout complexity. Test year 1808 can be consulted at plair.univ-rouen.fr.

In this paper, we describe DocExplore, an integrated software suite centered on the handling of digitized documents with an emphasis on ancient manuscripts. This software suite allows the augmentation and exploration of ancient documents of cultural interest. Specialists can add textual and multimedia data and metadata to digitized documents through a graphical interface that does not require technical knowledge. They are helped in this endeavor by sophisticated document analysis tools that allows for instance to spot words or patterns in images of documents. The suite is intended to ease considerably the process of bringing locked away historical materials to the attention of the general public by covering all the steps from managing a digital collection to creating interactive presentations suited for cultural exhibitions. Its genesis and sustained development reside in a collaboration of archivists, historians and computer scientists, the latter being not only in charge of the development of the software, but also of creating and incorporating novel pattern recognition for document analysis techniques.