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  • 1.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Foteini
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Conversational Systems in Machine Learning from the Point of View of the Philosophy of Science—Using Alime Chat and Related Studies2019In: Philosophies, ISSN 2409-9287, Vol. 4, no 3, article id 41Article in journal (Refereed)
    Abstract [en]

    This essay discusses current research efforts in conversational systems from the philosophy of science point of view and evaluates some conversational systems research activities from the standpoint of naturalism philosophical theory. Conversational systems or chatbots have advanced over the decades and now have become mainstream applications. They are software that users can communicate with, using natural language. Particular attention is given to the Alime Chat conversational system, already in industrial use, and the related research. The competitive nature of systems in production is a result of different researchers and developers trying to produce new conversational systems that can outperform previous or state-of-the-art systems. Different factors affect the quality of the conversational systems produced, and how one system is assessed as being better than another is a function of objectivity and of the relevant experimental results. This essay examines the research practices from, among others, Longino’s view on objectivity and Popper’s stand on falsification. Furthermore, the need for qualitative and large datasets is emphasized. This is in addition to the importance of the peer-review process in scientific publishing, as a means of developing, validating, or rejecting theories, claims, or methodologies in the research community. In conclusion, open data and open scientific discussion fora should become more prominent over the mere publication-focused trend.

  • 2. Ahmad, Riaz
    et al.
    Afzal, Muhammad Zeshan
    Rashid, Sheikh Faisal
    Liwicki, Marcus
    Breuel, Thomas
    Scale and Rotation Invariant OCR for Pashto Cursive Script using MDLSTM Network2015In: 13th International Conference on Document Analysis and Recognition, IEEE , 2015, p. 1101-1105Conference paper (Refereed)
    Abstract [en]

    Optical Character Recognition (OCR) of cursive scripts like Pashto and Urdu is difficult due the presence of complex ligatures and connected writing styles. In this paper, we evaluate and compare different approaches for the recognition of such complex ligatures. The approaches include Hidden Markov Model (HMM), Long Short Term Memory (LSTM) network and Scale Invariant Feature Transform (SIFT). Current state of the art in cursive script assumes constant scale without any rotation, while real world data contain rotation and scale variations. This research aims to evaluate the performance of sequence classifiers like HMM and LSTM and compare their performance with descriptor based classifier like SIFT. In addition, we also assess the performance of these methods against the scale and rotation variations in cursive script ligatures. Moreover, we introduce a database of 480,000 images containing 1000 unique ligatures or sub-words of Pashto. In this database, each ligature has 40 scale and 12 rotation variations. The evaluation results show a significantly improved performance of LSTM over HMM and traditional feature extraction technique such as SIFT. Keywords.

  • 3. Ahmad, Riaz
    et al.
    Afzal, Muhammad Zeshan
    Rashid, Sheikh Faisal
    Liwicki, Marcus
    Dengel, Andreas
    Breuel, Thomas
    Recognizable Units in Pashto Language for OCR2015In: 13th International Conference on Document Analysis and Recognition, IEEE , 2015, p. 1246-1250Conference paper (Refereed)
    Abstract [en]

    Atomic segmentation of cursive scripts into con- stituent characters is one of the most challenging problems in pattern recognition. To avoid segmentation in cursive script, concrete shapes are considered as recognizable units. Therefore, the objective of this work is to find out the alternate recognizable units in Pashto cursive script. These alternatives are ligatures and primary ligatures. However, we need sound statistical analysis to find the appropriate numbers of ligatures and primary ligatures in Pashto script. In this work, a corpus of 2, 313, 736 Pashto words are extracted from a large scale diversified web sources, and total of 19, 268 unique ligatures have been identified in Pashto cursive script. Analysis shows that only 7000 ligatures represent 91% portion of overall corpus of the Pashto unique words. Similarly, about 7, 681 primary ligatures are also identified which represent the basic shapes of all the ligatures.

  • 4.
    Alberti, M.
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Pondenkandath, V.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Wursch, M.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Ingold, R.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments2018In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 423-428, article id 8583798Conference paper (Refereed)
    Abstract [en]

    We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source(1), and accessible as Web Service through DIVAServices(2).

  • 5.
    Alberti, Michele
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Pondenkandath, Vinaychandran
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Würsch, Marcel
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Bouillon, Manuel
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Seuret, Mathias
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Ingold, Rolf
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Fribourg, Switzerland.
    Are You Tampering with My Data?2019In: Computer Vision – ECCV 2018 Workshops: Proceedings, Part II / [ed] Laura Leal-Taixé & Stefan Roth, Springer, 2019, p. 296-312Conference paper (Refereed)
    Abstract [en]

    We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks, causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.

  • 6.
    Byeon, Wonmin
    et al.
    University of Kaiserslautern, Kaiserslautern, Germany; German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Liwicki, Marcus
    University of Kaiserslautern, Kaiserslautern, Germany.
    Breuel, Thomas M
    University of Kaiserslautern, Kaiserslautern, Germany.
    Scene analysis by mid-level attribute learning using 2D LSTM networks and an application to web-image tagging2015In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 63, p. 23-29Article in journal (Refereed)
    Abstract [en]

    Abstract This paper describes an approach to scene analysis based on supervised training of 2D Long Short-Term Memory recurrent neural networks (LSTM networks). Unlike previous methods, our approach requires no manual construction of feature hierarchies or incorporation of other prior knowledge. Rather, like deep learning approaches using convolutional networks, our recognition networks are trained directly on raw pixel values. However, in contrast to convolutional neural networks, our approach uses 2D LSTM networks at all levels. Our networks yield per pixel mid-level classifications of input images; since training data for such applications is not available in large numbers, we describe an approach to generating artificial training data, and then evaluate the trained networks on real-world images. Our approach performed significantly better than others methods including Convolutional Neural Networks (ConvNet), yet using two orders of magnitude fewer parameters. We further show the experiment on a recently published dataset, outdoor scene attribute dataset for fair comparisons of scene attribute learning which had significant performance improvement (ca. 21%). Finally, our approach is successfully applied on a real-world application, automatic web-image tagging.

  • 7. Eichenberger, Nicole
    et al.
    Garz, Angelika
    Chen, Kai
    Wei, Hao
    Ingold, Rolf
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    DivaDesk: A Holistic Digital Workspace for Analyzing Historical Document Images2014In: Manuscript Cultures, ISSN 1867–9617, Vol. 7, p. 69-82Article in journal (Refereed)
    Abstract [en]

    In this article we present the concept of DIVADesk – a Virtual Research Environment (VRE) for scholarly work on historical documents inspired by the shift toward working with digital facsimiles. The contribution of this article is three-fold. First, a review of existing tools and projects shows that a holistic workspace integrating the latest outcomes of computational Document Image Analysis (DIA) research is still a desideratum that can only be achieved by intensive interdisciplinary collaboration. Second, the underlying modular architecture of the digital workspace is presented. It consists of a set of services that can be combined according to individual scholars’ requirements. Furthermore, interoperability with existing frameworks and services allows the research data to be shared with other VREs. The proposed DIVADesk addresses specific research with historical documents, as this is one of the hardest cases in computational DIA. The outcomes of this paradigmatic research can be transferred to other use cases in the humanities. The third contribution of this article is a description of already existing services and user interfaces to be integrated in DIVADesk. They are part of ongoing research at the DIVA research group at the University of Fribourg, Switzerland. The labeling tool DIVADIA, for example, provides methods for layout analysis, script analysis, and text recognition of historical documents. These methods build on the concept of incremental learning and provide users with semi-automatic labeling of document parts, such as text, images, and initials. The conception and realization of DIVADesk promises research outcomes both in computer science and in the humanities. Therefore, an interdisciplinary approach and intensive collaboration between scholars in the two research fields are of crucial importance.

  • 8.
    Kolsch, A.
    et al.
    MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
    Mishra, A.
    MindGarage, University of Kaiserslautern, Germany.
    Varshneya, S.
    MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
    Afzal, M.Z
    MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. MindGarage, University of Kaiserslautern, Germany; Insiders Technologies GmbH, Kaiserslautern, Germany; University of Fribourg, Switzerland.
    Recognizing challenging handwritten annotations with fully convolutional networks2018In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 25-31, article id 8563221Conference paper (Refereed)
    Abstract [en]

    This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: Background and handwritten annotation. The best model achieves a mean Intersection over Union (IOU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.

  • 9.
    Maergner, Paul
    et al.
    Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
    Pondenkandath, Vinaychandran
    Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
    Alberti, Michele
    Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Riesen, Kaspar
    Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.
    Ingold, Rolf
    Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
    Fischer, Andreas
    Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.Institute of Complex Systems, University of Applied Sciences and Arts Western Switzerland, Fribourg, Switzerland.
    Combining graph edit distance and triplet networks for offline signature verification2019In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 125, p. 527-533Article in journal (Refereed)
    Abstract [en]

    Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.

  • 10.
    Pondenkandath, V.
    et al.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Alberti, M.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Eichenberger, N.
    Staatsbibliothek zu Berlin, Preußischer Kulturbesitz, Berlin, Germany.
    Ingold, R.
    Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Liwicki, Marcus
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Document Image and Voice Analysis Group (DIVA), University of Fribourg, Switzerland.
    Identifying cross-depicted historical motifs2018In: Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, IEEE, 2018, p. 333-338, article id 8583783Conference paper (Refereed)
    Abstract [en]

    Cross-depiction is the problem of identifying the same object even when it is depicted in a variety of manners.This is a common problem in handwritten historical document image analysis, for instance when the same letter or motif is depicted in several different ways. It is a simple task for humans yet conventional computer vision methods struggle to cope with it. In this paper we address this problem using state-of-the-art deep learning techniques on a dataset of historical watermarks containing images created with different methods of reproduction, such as hand tracing, rubbing, and radiography.To study the robustness of deep learning based approaches to the cross-depiction problem, we measure their performance on two different tasks: Classification and similarity rankings. For the former we achieve a classification accuracy of 96 % using deep convolutional neural networks. For the latter we have a false positive rate at 95% recall of 0.11. These results outperform state-of-the-art methods by a significant margin

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