Emergent states are temporal group phenomena that arise from collective affective, behavioral, and cognitive processes shared among the group's members during their interactions. Cohesion is one such state, mainly conceptualized by scholars as affective in nature, and frequently distinguished into the two dimensions social and task cohesion. Whereas social cohesion is related to the need of belonging to a group, task cohesion is related to the group's goals and tasks. In this article, we emphasize the importance of behavioral interaction dynamics to predict cohesion's dynamics. Drawing from Social Science insights, we investigate the interplay between social and task cohesion to predict their dynamics across group tasks from nonverbal behavioral features. Three computational architectures exploiting transfer learning are presented. Transfer learning capitalizes on information learnt by a model for a specific dimension to predict the dynamics of the other dimension. Results show that integrating the influence of social cohesion to predict dynamics of task cohesion outperforms state-of-the-art. To predict dynamics of social cohesion, a model integrating the reciprocal impact of social and task cohesion significantly improves performance with respect to the state-of-the-art and a model only integrating the impact of task cohesion on dynamics of social cohesion.

Cohesion is an affective group phenomenon. It has received a lot of attention from scholars both in Social Sciences and in Affective Computing that showed that cohesion and emotion influence each other, highlighting the need to jointly analyze them. This study presents 2 deep neural network architectures grounded on multitask learning to jointly predict cohesion and emotion. Inspired by 2 major Social Sciences approaches on group emotion (i.e., Top-down and Bottom-up), these architectures exploit cohesion and emotion interdependencies intending to improve the prediction of the dynamics (i.e. changes over time) of the Social and Task dimensions of cohesion. Emotion, here, is addressed in terms of its valence. Both architectures are evaluated against the performances of a similar model that only predicts the dynamics of both the Social and Task dimensions of cohesion, without integrating valence. Statistical analysis shows that only the deep model implementing the Bottom-up approach significantly improved the predictions of the Task cohesion’s dynamics. This result confirms the theoretical and practical benefits of multitasking as it takes full advantage of the inherent relationships between group emotion and cohesion to improve Task cohesion’s predictions.

Emergent states are behavioral, cognitive and affective processes appearing among the members of a group when they interact together. In the last decade, the development of computational approaches received a growing interest in building Human-Centered systems. Such a development is particularly difficult because some of these states have several dimensions interplaying somehow and somewhere over time. In this paper, we focus on cohesion, its dimensions and their interplay. Several definitions of cohesion exist, it can be simply defined as the tendency of a group to stick together to pursue goals and/or affective needs. This plethora of definitions resulted in many different cohesion dimensions. Social and Task dimensions are the most investigated both in Social Sciences and Computer Science since they both play an important role in a wide range of contexts and groups. To the best of our knowledge, however, no previous work on the prediction of cohesion dynamics focused on how these 2 dimensions interplay. We leverage Social Sciences to address this issue. In particular, we take advantage of the importance of Social cohesion for creating flexible and constructive relationships to reinforce Task cohesion. We describe a Deep Neural Network architecture (DNN) for predicting the dynamics of Task cohesion by applying transfer learning from a pre-trained model dedicated to the prediction of Social cohesion dynamics. Our architecture is evaluated against several baselines. Results show that it significantly improves the predictions of the Task cohesion dynamics, confirming the benefits of integrating Social Sciences insights into models architectures.

Historical documents present many challenges for offline handwriting recognition systems, among them, the segmentation and labeling steps. Carefully annotated text lines are needed to train an HTR system. In some scenarios, transcripts are only available at the paragraph level with no text-line information. In this work, we demonstrate how to train an HTR system with few labeled data. Specifically, we train a deep convolutional recurrent neural network (CRNN) system on only 10% of manually labeled text-line data from a dataset and propose an incremental training procedure that covers the rest of the data. Performance is further increased by augmenting the training set with specially crafted multi scale data. We also propose a model-based normalization scheme which considers the variability in the writing scale at the recognition phase. We apply this approach to the publicly available READ dataset. Our system achieved the second best result during the ICDAR2017 competition [1].

The detection of negative emotions through daily activities such as writing and drawing is useful for promoting wellbeing. The spread of human-machine interfaces such as tablets makes the collection of handwriting and drawing samples easier. In this context, we present a first publicly available database which relates emotional states to handwriting and drawing, that we call EMOTHAW (EMOTion recognition from HAndWriting and draWing). This database includes samples of 129 participants whose emotional states, namely anxiety, depression, and stress, are assessed by the Depression-Anxiety-Stress Scales (DASS) questionnaire. Seven tasks are recorded through a digitizing tablet: pentagons and house drawing, words copied in handprint, circles and clock drawing, and one sentence copied in cursive writing. Records consist in pen positions, on-paper and in-air, time stamp, pressure, pen azimuth, and altitude. We report our analysis on this database. From collected data, we first compute measurements related to timing and ductus. We compute separate measurements according to the position of the writing device: on paper or in-air. We analyze and classify this set of measurements (referred to as features) using a random forest approach. This latter is a machine learning method, based on an ensemble of decision trees, which includes a feature ranking process. We use this ranking process to identify the features which best reveal a targeted emotional state. We then build random forest classifiers associated with each emotional state. We provide accuracy, sensitivity, and specificity evaluation measures obtained from cross-validation experiments. Our results show that anxiety and stress recognition perform better than depression recognition.

This paper reports a new approach based on convolutional neural networks (CNNs), which uses spatial transformer networks (STNs). The approach, referred to as Tied Spatial Transformer Networks (TSTNs), consists of training a system which combines a localization CNN and a classification CNN whose weights are shared. The localization CNN is used for predicting an affine transform for the input image, which is then processed according to the predicted parameters and passed through the classification CNN. We have conducted initial experiments on the cluttered MNIST dataset of noisy digits, comparing the TSTN and STN with identical configurations of trainable parameters, but untied, as well as the classification CNN only, applied to the unprocessed images. In all these cases, we obtain better results using the TSTN. We conjecture that the TSTN provides a regularization effect, as compared to untied STNs. Further experiments seem to support this hypothesis.

Arabic handwritten documents present specific challenges due to the cursive nature of the writing and the presence of diacritical marks. Moreover, one of the largest labeled database of Arabic handwritten documents, the OpenHart-NIST database includes specific noise, namely guidelines, that has to be addressed. We propose several approaches to process these documents. First a guideline detection approach has been developed, based on K-means, that detects the documents that include guidelines. We then propose a series of preprocessing at text-line level to reduce the noise effects. For text-lines including guidelines, a guideline removal preprocessing is described and existing keystroke restoration approaches are assessed. In addition, we propose a preprocessing that combines noise removal and deskewing by removing line fragments from neighboring text lines, while searching for the principal orientation of the text-line. We provide recognition results, showing the significant improvement brought by the proposed processings.

Handwriting recognition systems usually rely on static dictionaries and language models. Full coverage of these dictionaries is generally not achieved when dealing with unrestricted document corpora due to the presence of Out-Of-Vocabulary words. In a previous work, dynamic dictionaries were built from Web resources and successfully applied to isolated word recognition. In the present work we extend this approach to text-line recognition. Line segmentation into words is needed to exploit dynamic dictionaries and it is performed using BLSTM classifiers to align filler models and word sequence outputs. Words are then classified based on the confidence score into anchor and non-anchor words (AWs and NAWs). AWs are equated to the BLSTM outputs and used as such. Dynamic dictionaries are built for NAWs by exploiting Web resources for their character sequence and for neighboring AWs. Text-lines are decoded again using dynamic dictionaries and re-estimated language model. We conduct experiments on the publicly available RIMES database and show that the introduction of the dynamic dictionary is beneficial. Equally important, we show that the gain increases as the proportion of OOVs increases.

This paper deals with the recognition of free-style handwritten text lines. We compare 2 state-of-the-art segmentation-free recognition approaches. The first one is the popular context-dependent HMM approach (Hidden Markov Models). The second one is the recent BLSTM (Bi-directional Long Short-Term Memory) approach based on recurrent neural networks and memory blocks. For the sake of comparison, both recognizers use the same set of features and language model. They are compared from the following perspectives: sliding window parameters for feature extraction, training and decoding speed and performance accuracy with or without using a language model. We compare these two approaches on the publicly available Rimes database of French handwritten mails. Our main findings are that long frame sequences, obtained with specific window parameters, improve both recognizers, and that BLSTMs outperform HMMs in terms of WER rates, at the expense of considerably longer training times.

Handwriting recognition systems rely on predefined dictionaries obtained from training data. Small and static dictionaries are usually exploited to obtain high in-vocabulary (IV) accuracy at the expense of coverage. Thus the recognition of out-of-vocabulary (OOV) words cannot be handled efficiently. To improve OOV recognition while keeping IV dictionaries small, we introduce a multi-step approach that exploits Web resources. After an initial IV-OOV sequence classification, external resources are used to create OOV sequence-adapted dynamic dictionaries. A final Viterbi-based decoding is performed over the dynamic dictionary to determine the most probable word for the OOV sequence. We validate our approach with experiments conducted on RIMES, a publicly available database. Results show that improvements are obtained compared to standard handwriting recognition, performed with a static dictionary. Both domain adapted and generic dynamic dictionaries are studied and we show that domain adaptation is beneficial.

This paper proposes an approach for segmenting a Web page into its semantic parts. Such analysis may be useful for adapting blog or other pages on small devices. In this approach, we take advantage of both dynamic layout after rendering and textual information. Our method segments the page into blocks and then classifies the blocks. A classification in semantic parts is performed thanks to a SVM-based machine learning approach using a set of 30 textual and visual-based features. Evaluation is conducted on a Web blog database. Results are provided for both block classification and blog segmentation into articles.

We propose a novel scenario called "writer retrieval" consisting in the retrieval from a set of documents all those produced by the same writer. The retrieval is based on a set of ten features correlated to perceived characteristics of the writing (orientation, regularity...). These features are extracted from the probability density function of the orientations of the writing contour. We evaluate the overall efficiency of each feature and the relative importance of features according to queries. Evaluation is conducted on the publicly available IAM database.

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

This article describes the isolated word recognizer presented by the authors to the ICDAR 2009 French handwriting recognition competition. The system is a combination of three isolated word recognizers based on different features and models. A novel n-best combination method is proposed and compared to standard combination methods. New results on the ICDAR 2009 test database are reported.

We compare in this study two image restoration approaches for the pre-processing of printed documents:namely the Non-local Means filter and a total variation minimization approach. We apply these two approaches to printed document sets from various periods,and we evaluate their effectiveness through character recognition performance using an open source OCR. Our results show that for each document set, one or both pre-processing methods improve character recog-nition accuracy over recognition without preprocessing. Higher accuracies are obtained with Non-local Means when characters have a low level of degradation since they can be restored by similar neighboring parts of non-degraded characters. The Total Variation approach is more effective when characters are highly degraded and can only be restored through modeling instead of using neighboring data.

The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.

We present an identification and authentication system based on hand modality which is part of a reference system for all modalities developed within the biosecure consortium. It relies on simple geometric features extracted from hand boundary. The different steps of this system are detailed, namely: pre-processing, feature extraction and hand matching. This system has been tested on the biosecure hand database which consists of 4500 hand images of 750 individuals. Results are detailed with respect to different enrolment conditions such as population size, enrolment size, and image resolution.

In this paper we present a two-stage system for the off-line recognition of cursive Arabic handwritten words. The proposed method is analytic without segmentation, and is able to cope with handwriting inclination and with shifted positions of diacritical marks. First, the recognition stage relies on 3 classifiers based on hidden Markov modelling (HMM). The second stage depends on the combination of these classifiers. The feature vectors used for recognition are related to pixel density distribution and to local pixel configurations. These vectors are extracted on word binary images by using a sliding window approach with different angles. We have experimented different combination schemes. The neural network-based combined system yields best performance on the IFN- ENIT benchmark data base of handwritten names of Tunisian villages/towns.

This paper investigates the application of dynamic Bayesian networks (DBNs) to the recognition of degraded characters from historical printed books. This framework allows us to capture the 2D nature of character images by the coupling of two HMMs (Hidden Markov Models). The vertical HMM observes image columns while the horizontal HMM observes image rows respectively. Two coupled DBN architectures are proposed to model interactions between these two streams. We present experiments on real degraded characters extracted from an ancient printed book (17th century). These experiments demonstrate that coupled architectures significantly better cope with broken characters than non coupled ones and than discriminative methods such as SVMs.

This paper presents a text line segmentation method for printed or handwritten historical Arabic documents. Documents are first classified into 2 classes using a K-means scheme. These classes correspond to document complexity (easy or not easy to segment). Then, a document which includes overlapping and touching characters, is divided into vertical strips. The extracted text blocks obtained by horizontal projection are classified into three categories: small, average and large text blocks. After segmenting the large text blocks, the lines are obtained by matching adjacent blocks within two successive strips using spatial relationship. The document without overlapping or touching characters is segmented by making abstraction on the segmentation module of the large text blocks. The text line segmentation method has a 96% accuracy on a collection of 100 historical documents

In this paper, we describe a 1D HMM offline handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline-dependant features.

In the frame of a unified messaging system, a crucial task of the system is to provide the user with key information on every message received, like keywords reflecting the object of the message, or the name of the sender. However, in the case of facsimiles, this information is not as easy to detect as in the case of e-mails, since no standard headers are defined. The aim of the presented work is to identify and extract specific information (the name of the sender) from a fax cover page. For this purpose, methods based on image document analysis (OCR recognition, physical blocks selection), and text analysis methods (optimized dictionary lookup, local grammar rules), are implemented to work in parallel. The fusion of their results brings a more accurate guess than any of the methods would achieve separately.

We discuss the name block location process of a messaging system which deals with facsimile images including handwritten items. This process is the first stage of an information extraction task which aims at extracting condensed information from such documents. The approach is based on the use of spatial/visual cues for selecting regions in the image where sender and recipient information can be found. These cues are based on the human visual grouping process. The start of the process is the binary image and its electronic version produced by an OCR system. A document processing stage first extracts layout components at a pseudo word level and detects handwritten components. The electronic version enables the selection of layout components including predefined printed keywords. These layout components are then grouped under spatial constraints to initialize a logical description. The logical objects searched for are the main field headers relative to the sender and the recipient and the sender name. Experimental results on the performance of the approach are presented.

Presents the first achievements of HERS (Hypermedia Edit and Read Station), which is devoted to the browsing and editing of hypermedia documents from literary material including document images. Concerning the editing, our purpose as twofold. First, capabilities are offered to transcribe manuscripts. Transcribing the text consists of coupling lines typed on the keyboard with their corresponding text lines in the manuscript images. A collaborative system, based on computer-human interaction and document analysis, is proposed for performing this task. Second, interactive tools are offered to organize the electronic document and establish hypermedia links between its different components (image areas, transcribed words or lines, or other kinds of heterogeneous data). Concerning the browsing, we developed an approach based on information visualization in order to provide users with an idea of the overall organization of the hyperdocument and so help them to navigate through it.

The method herein proposed detects text lines on handwritten pages which may include either lines oriented in several directions, erasures, or annotations between main lines. The method has a hypothesis-validation strategy which is iteratively activated until the end of the segmentation is reached. At each stage of the process, the best text-line hypothesis is generated in the Hough domain. Taking into account the fluctuations of the text-line components. Afterwards, the validity of the line is checked in the image domain using a proximity criteria which analyses the context in which is perceived the alignment hypothesized. Ambiguous components belonging to several text lines are also marked.