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This paper presents GAME-ON (Group Analysis of Multimodal Expression of cohesiON), a multimodal dataset specifically designed for studying group cohesion and for explicitly controlling its variation over time. Cohesion is here addressed according to the Severt and Estrada's theoretical multidimensional integrative framework. More specifically, GAME-ON focuses on the social and task dimensions of the instrumental function of cohesion. The dataset consists of over 11 hours of synchronized multimodal recordings (audio, video, and motion capture data) of 17 small groups (3 persons) playing a social game, i.e., an escape game. The game consists of several tasks designed to manipulate the variation of cohesion over time. GAME-ON includes annotations consisting of self-assessment of cohesion and other constructs such as emotions, leadership, and warmth and competence. A first statistical analysis of these annotations shows that we successfully manipulated all the relative variations of cohesion (between tasks) over time. This holds for all tasks except for one where we observed a significant variation of cohesion in the opposite direction than expected. The dataset will be publicly available for research purposes. The motivation of our work is to provide the scientific community with an asset for studying cohesion and other group phenomena.

Slanted text has been demonstrated to be a salient feature of handwriting. Its estimation is a necessary preprocessing task in many document image processing systems in order to improve the required training. This paper describes and evaluates a new technique for removing the slant from historical document pages that avoids the segmentation procedure into text lines and words. The proposed technique first relies on slant angle detection from an accurate selection of fragments. Then, a slant removal technique is applied. However, the presented slant removal technique may be combined with any other slant detection algorithm. Experimental results are provided for four document image databases: two historical document databases, the TrigraphSlant database (the only database dedicated to slant removal), and a printed database in order to check the precision of the proposed technique.

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The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.