Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format. Current state-of-the-art works lack databases and baseline benchmarks for digitizing mobile captured documents. We have organized a competition for mobile document capture and OCR in order to address this issue. The competition is structured into two independent challenges: smartphone document capture, and smartphone OCR. This report describes the datasets for both challenges along with their ground truth, details the performance evaluation protocols which we used, and presents the final results of the participating methods. In total, we received 13 submissions: 8 for challenge-1, and 5 for challenge-2.

On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learning-based methods has been noted for solving various sub-fields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learning-based method for text line segmentation of historical documents. The proposed method is based on using an adaptive U-Net architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the state-of-the-art methods.

For patients suffering from Parkinson's disease with severe movement disorders, functional surgery may be required when medical therapy is not effective. In Deep Brain Stimulation (DBS), electrodes are implanted within the brain to stimulate deep structures such as SubThalamic Nucleus (STN). The quality of patient surgical outcome is generally related to the accuracy of nucleus targeting during surgery. In this paper, we focused on identifying optimum sites for STN DBS by studying symptomatic motor improvement along with neuropsychological side effects. We described successive steps for constructing digital atlases gathering patient's location of electrode contacts automatically segmented from postoperative images, and clinical scores. Three motor and five neuropsychological scores were included in the study. Correlations with active contact locations were carried out using an adapted hierarchical ascendant classification. Such analysis enabled the extraction of representative clusters to determine the optimum site for therapeutic STN DBS. For each clinical score, we built an anatomo-clinical atlas representing its improvement or deterioration in relation with the anatomical location of electrodes and from a population of implanted patients. To the best of our knowledge, we reported for the first time a discrepancy between a very good motor improvement by targeting the postero-superior region of the STN and an inevitable deterioration of the categorical and phonemic fluency in the same region. Such atlases and associated analysis may help better understanding of functional mapping in deep structures and may help pre-operative decision-making process and especially targeting.

Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical devices. Compared with classical algorithms, deep learning (DL) architectures have demonstrated superior accuracy and high generalization capacity. Furthermore, they can be embedded on edge devices for real-time inference. 3D vectorcardiograms (VCG) provide a unifying framework for detecting R-peaks regardless of the acquisition strategy or number of ECG leads. In this article, a DL architecture was demonstrated to provide enhanced precision when trained and applied on 3D VCG, with no pre-processing nor post-processing steps. Experiments were conducted on four different public databases. Using the proposed approach, high F1-scores of 99.80% and 99.64% were achieved in leave-one-out cross-validation and cross-database validation protocols, respectively. False detections, measured by a precision of 99.88% or more, were significantly reduced compared with recent state-of-the-art methods tested on the same databases, without penalty in the number of missed peaks, measured by a recall of 99.39% or more. This approach can provide new applications for devices where precision, or positive predictive value, is essential, for instance cardiac magnetic resonance imaging.

International audienceTexture feature analysis has undergone tremendous growth in recent years. It plays an important role for the analysis of many kinds of images. More recently, the use of texture analysis techniques for historical document image segmentation has become a logical and relevant choice in the conditions of significant document image degradation and in the context of lacking information on the document structure such as the document model and the typographical parameters. However, previous work in the use of texture analysis for segmentation of digitized historical document images has been limited to separately test one of the well-known texture-based approaches such as autocorrelation function, Grey Level Co-occurrence Matrix (GLCM), Gabor filters, gradient, wavelets, etc. In this paper we raise the question of which texture-based method could be better suited for discriminating on the one hand graphical regions from textual ones and on the other hand for separating textual regions with different sizes and fonts. The objective of this paper is to compare some of the well-known texture-based approaches: autocorrelation function, GLCM, and Gabor filters, used in a segmentation of digitized historical document images. Texture features are briefly described and quantitative results are obtained on simplified historical document images. The achieved results are very encouraging