Jean-Marc Ogier

Bio: Jean-Marc Ogier is an academic researcher from University of La Rochelle. The author has contributed to research in topics: Image retrieval & Image segmentation. The author has an hindex of 25, co-authored 254 publications receiving 2620 citations. Previous affiliations of Jean-Marc Ogier include University of Rouen.

ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT

Abstract: Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.

ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019

Abstract: With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

Symbol and character recognition: application to engineering drawings

Abstract: In this paper, we consider the general problem of technical document interpretation, as applied to the documents of the French Telephonic Operator, France Telecom. More precisely, we focus the content of this paper on the computation of a new set of features allowing the classification of multioriented and multiscaled patterns. This set of invariants is based on the Fourier–Mellin Transform. The interests of this computation rely on the excellent classification rate obtained with this method and also on using this Fourier–Mellin transform within a “filtering mode”, with which we can solve the well known difficult problem of connected character recognition.

eBDtheque: A Representative Database of Comics

Abstract: We present eBDtheque, a database of various comic book images and their ground truth for panels, balloons and text lines plus semantic annotations. The database consists of a hundred pages of various comic book albums, Franco-Belgian, American comics and mangas. Additionally, we present the piece of software used to establish the ground truth and a tool to validate results against this ground truth. Everything is publicly available for scientific use on http://ebdtheque.univ-lr.fr.

A comprehensive survey of mostly textual document segmentation algorithms since 2008

Abstract: In document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community.

Recent advances in convolutional neural networks

Abstract: We give an overview of the basic components of CNN.We discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.We introduce the applications of CNN on various tasks, including image classification, object detection, object tracking, pose estimation, text detection, visual saliency detection, action recognition, scene labeling, speech and natural language processing.We discuss the challenges in CNN and give several future research directions. In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

Recent Advances in Convolutional Neural Networks

Abstract: In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging on the rapid growth in the amount of the annotated data and the great improvements in the strengths of graphics processor units, the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. We detailize the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation. Besides, we also introduce various applications of convolutional neural networks in computer vision, speech and natural language processing.

Références bibliographiques: La fibroscopie bronchique

Abstract: Méthodes : Sur une série de 31 femmes présentant des varices périnéales d’origine extrasaphéniennes ayant bénéficié d’une exploration radiologique veineuse pelvienne et de l’embolisation des veines incontinentes nous avons étudié les résultats et les corrélations anatomo-clinique entre les varices et les signes cliniques de l’insuffisance veineuse pelvienne. Un sous-groupe B de 23 patientes présentant une insuffisance veineuse pelvienne et des signes cliniques a été comparé à un groupe A de 4 patientes qui ne présentaient pas d’insuffisance veineuse pelvienne à l’exploration. Nous avons choisi d’étudier 3 signes cliniques : la douleur pelvienne et la douleur des membres inférieurs survenant au début ou pendant les règles ainsi que la dyspareunie. La symptomatologie a été évaluée sur une échelle analogique de 0 à 10 et un score clinique (de 0 à 30) a été établi en ajoutant ces 3 chiffres.

A Survey of Digital Map Processing Techniques

Abstract: Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.