This paper presents the results of the ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers). This work aims at providing a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition. At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set 2000 images with their ground truths. In the first task-Script classificationthe classifiers have been evaluated by a test set of 1000 single-type manuscripts. In the second task, a-Fuzzy Classification‖ has been carried out on a set of 2000 multiscript-type manuscripts. The results of the participants provide the first baseline evaluation up to the accuracy score of 83.9% for the task 1 and to the fuzzy weighted score of 2.96/4 for the task 2. An analysis based on the intra-class distance and matrix of confusion of each classifier is also given.

The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results.

This article presents a method for generating semi-synthetic images of old documents where the pages might be torn (not flat). By using only 2D deformation models, most existing methods give non-realistic synthetic document images. Thus, we propose to use 3D approach for reproducing geometric distortions in real documents. First, a new proposed texture coordinate generation technique extracts texture coordinates of each vertex in the document shape (mesh) resulting from 3D scanning of a real degraded document. Then, any 2D document image can be overlayed on the mesh by using an existing texture image mapping method. As a result, many complex real geometric distortions can be integrated in generated synthetic images. These images then can be used for enriching training sets or for performance evaluation. The degradation method here is jointly used with the character degradation model we proposed in [1] to generate the 6000 semi-synthetic degraded images of the music score removal staff line competition of ICDAR 2013 1 .

Introduction Isavuconazole, a triazole antifungal, is an inhibitor of cytochrome P450 3A4, which also metabolizes tacrolimus and sirolimus. In previous studies, isavuconazole administration increased tacrolimus and sirolimus area under the curve values by 2.3‐fold and 1.8‐fold, respectively, in healthy adults and tacrolimus concentration/dose (C/D) ratio by 1.3‐fold in solid organ transplant patients. We aimed to determine the magnitude of effect of isavuconazole administration on tacrolimus and sirolimus C/D ratios in allogeneic hematopoietic stem cell transplant (alloHSCT) patients. Methods A retrospective, single‐center, single‐arm study in adult alloHSCT patients who received at least 10 days of combination therapy with isavuconazole and tacrolimus and/or sirolimus as inpatients or outpatients was conducted. Tacrolimus and sirolimus trough serum concentrations were measured up to twice weekly for up to 4 weeks. Results Twenty‐two patients receiving tacrolimus and twenty patients receiving sirolimus met the inclusion criteria. The mean C/D ratio increased from baseline by 1.42‐fold for tacrolimus during week 1 (P = 0.002) and up to 1.56‐fold for sirolimus during week 2 (P = 0.02). For the remaining timepoints, tacrolimus and sirolimus C/D ratios were not statistically significantly different from baseline. Conclusion In alloHSCT patients, modest increases in tacrolimus and sirolimus C/D ratios from baseline were observed within the first 2 weeks after initiation of isavuconazole.

Historical documents pose challenging problems for training handwriting recognition systems. Besides the high variability of character shapes inherent to all handwriting, the image quality can also differ greatly, for instance due to faded ink, ink bleed-through, wrinkled and stained parchment. Especially when only few learning samples are available, it is difficult to incorporate this variability in the morphological character models. In this paper, we investigate the use of image degradation to generate synthetic learning samples for historical handwriting recognition. With respect to three image degradation models, we report significant improvements in accuracy for recognition with hidden Markov models on the medieval Saint Gall and Parzival data sets.

This paper presents an efficient parametrization method for generating synthetic noise on document images. By specifying the desired categories and amount of noise, the method is able to generate synthetic document images with most of degradations observed in real document images (ink splotches, white specks or streaks). Thanks to the ability of simulating different amount and kind of noise, it is possible to evaluate the robustness of many document image analysis methods. It also permits to generate data for algorithms that employ a learning process. The degradation model presented in [7] needs eight parameters for generating randomly noise regions. We propose here an extension of this model which aims to set automatically the eight parameters to generate precisely what a user wants (amount and category of noise). Our proposition consists of three steps. First, Nsp seed-points (i.e. centres of noise regions) are selected by an adaptive procedure. Then, these seed-points are classified into three categories of noise by using a heuristic rule. Finally, each size of noise region is set using a random process in order to generate degradations as realistic as possible.

For the segmentation of ancient digitized document images, it has been shown that texture feature analysis is a consistent choice for meeting the need to segment a page layout under significant and various degradations. In addition, it has been proven that the texture-based approaches work effectively without hypothesis on the document structure, neither on the document model nor the typographical parameters. Thus, by investigating the use of texture as a tool for automatically segmenting images, we propose to search homogeneous and similar content regions by analyzing texture features based on a multiresolution analysis. The preliminary results show the effectiveness of the texture features extracted from the autocorrelation function, the Grey Level Co-occurrence Matrix (GLCM), and the Gabor filters. In order to assess the robustness of the proposed texture-based approaches, images under numerous degradation models are generated and two image enhancement algorithms (non-local means filtering and superpixel techniques) are evaluated by several accuracy metrics. This study shows the robustness of texture feature extraction for segmentation in the case of noise and the uselessness of a denoising step.

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