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Author: search.filters.author.Hadjadj, Zineb

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    Applied Gaming-Based Emotion- Driven on Disaster Resilience Training
    (CERIST, 2024-11) Hadjar, Hayette; Hemmje, Matthias; Hadjadj, Zineb; Meziane, Abdelkrim
    Managing stress in disaster response environments is a critical challenge that requires effective strategies to enhance the resilience and well-being of emergency responders. This study introduces DisasterPlay, a prototype web-based platform designed for resilience training. The prototype features a comprehensive model design, user interface, and implementation using WebXR, facial emotion monitoring, and contactless vital signs monitoring. This approach not only improves the training experience but also aids decisionmakers in selecting the most suitable candidates for high-stakes tasks, thereby enhancing resource allocation. Accessible via web browsers and utilizing cloud-based data processing, this innovative platform aims to provide a robust solution for advancing disaster response strategies.
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    Binarization of Document Images with Various Object Sizes
    (CERIST, 2017-02-01) Hadjadj, Zineb; Meziane, Abdelkrim
    There are a lot of document image binarization techniques that try to differentiate between foreground and background but many of them fail to correctly detect all the text pixels because of degradations. In this paper, a new binarization method for document images is presented. The proposed method is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus for some documents, the content may not be retrieved correctly. In this paper we try to overcome one of the limitations of Sauvola’s binarization which is the Handling badly various object sizes. The well-known Chan-Vese active contour model is use in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both small and large objects inside a single document, without adjusting manually the window size to the document content. The efficiency of the proposed method is shown on several document images with various object sizes.
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    A tracking approach for text line segmentation in handwritten documents
    (CERIST, 2017-02-24) Setitra, Insaf; Hadjadj, Zineb; Meziane, Abdelkrim
    Tracking of objects in videos consists of giving a label to the same object moving in different frames. This labeling is performed by predicting position of the object given its set of features observed in previous frames. In this work, we apply the same rationale by considering each connected component in the manuscript as a moving object and to track it so that to minimize the distance and angle of the connected component to its nearest neighbor. The approach was applied to images of ICDAR 2013 handwritten segmentation contest and proved to be robust against text orientation, size and writing script.
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    ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization
    (CERIST, 2015-10-12) Hadjadj, Zineb; Meziane, Abdelkrim; Cherfa, Yazid; Cheriet, Mohamed
    Binarization of historical documents is difficult and is still an open area of research. In this paper, a new binarization technique for document images is presented. The proposed technique is based on the most commonly used binarization method: Sauvola's, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola's formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus on documents such as magazines, the content may not be retrieved correctly. In this paper we use the image contrast that is defined by the local image minimum and maximum in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both low and correctly contrasted objects inside a single document, without adjusting manually the user-defined parameters to the document content. The efficiency of the proposed method is shown on both recent and historical document images of the datasets that are used in DIBCO datasets including different types of degradations.
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    Binarization of Degraded Historical Document Images
    (CERIST, 2014) Hadjadj, Zineb; Meziane, Abdelkrim; Cheriet, Mohamed; Cherfa, Yazid
    Old document images often suffer from different types of degradation that render their binarization a challenging task. In this paper, a new binarization algorithm for degraded document images is presented. The method is based on active contours evolving according to intrinsic geometric measures of the document image; Niblack’s thresholding is also used to control the active contours propagation. The validity of the proposed method is demonstrated on both recent and historical document images including different types of degradations, the results are compared with a number of known techniques in the literature