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  • 1.
    Ayyalasomayajula, Kalyan Ram
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wilkinson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    CalligraphyNet: Augmenting handwriting generation with quill based stroke width2019Manuscript (preprint) (Other academic)
    Abstract [en]

    Realistic handwritten document generation garners a lot ofinterest from the document research community for its abilityto generate annotated data. In the current approach we haveused GAN-based stroke width enrichment and style transferbased refinement over generated data which result in realisticlooking handwritten document images. The GAN part of dataaugmentation transfers the stroke variation introduced by awriting instrument onto images rendered from trajectories cre-ated by tracking coordinates along the stylus movement. Thecoordinates from stylus movement are augmented with thelearned stroke width variations during the data augmentationblock. An RNN model is then trained to learn the variationalong the movement of the stylus along with the stroke varia-tions corresponding to an input sequence of characters. Thismodel is then used to generate images of words or sentencesgiven an input character string. A document image thus cre-ated is used as a mask to transfer the style variations of the inkand the parchment. The generated image can capture the colorcontent of the ink and parchment useful for creating annotated data.

  • 2.
    Wilkinson, Tomas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Learning based Word Search and Visualisation for Historical Manuscript Images2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Today, work with historical manuscripts is nearly exclusively done manually, by researchers in the humanities as well as laypeople mapping out their personal genealogy. This is a highly time consuming endeavour as it is not uncommon to spend months with the same volume of a few hundred pages. The last few decades have seen an ongoing effort to digitise manuscripts, both preservation purposes and to increase accessibility. This has the added effect of enabling the use methods and algorithms from Image Analysis and Machine Learning that have great potential in both making existing work more efficient and creating new methodologies for manuscript-based research.

    The first part of this thesis focuses on Word Spotting, the task of searching for a given text query in a manuscript collection. This can be broken down into two tasks, detecting where the words are located on the page, and then ranking the words according to their similarity to a search query. We propose Deep Learning models to do both, separately and then simultaneously, and successfully search through a large manuscript collection consisting of over a hundred thousand pages.

    A limiting factor in applying learning-based methods to historical manuscript images is the cost, and therefore, lack of annotated data needed to train machine learning models. We propose several ways to mitigate this problem, including generating synthetic data, augmenting existing data to get better value from it, and learning from pre-existing, partially annotated data that was previously unusable.

    In the second part, a method for visualising manuscript collections called the Image-based Word Cloud is proposed. Much like it text-based counterpart, it arranges the most representative words in a collection into a cloud, where the size of the words are proportional to their frequency of occurrence. This grants a user a single image overview of a manuscript collection, regardless of its size. We further propose a way to estimate a manuscripts production date. This can grant historians context that is crucial for correctly interpreting the contents of a manuscript.

  • 3.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A novel word segmentation method based on object detection and deep learning2015In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I / [ed] Bebis, G; Boyle, R; Parvin, B; Koracin, D; Pavlidis, I; Feris, R; McGraw, T; Elendt, M; Kopper, R; Ragan, E; Ye, Z; Weber, G, Springer, 2015, p. 231-240Conference paper (Refereed)
    Abstract [en]

    The segmentation of individual words is a crucial step in several data mining methods for historical handwritten documents. Examples of applications include visual searching for query words (word spotting) and character-by-character text recognition. In this paper, we present a novel method for word segmentation that is adapted from recent advances in computer vision, deep learning and generic object detection. Our method has unique capabilities and it has found practical use in our current research project. It can easily be trained for different kinds of historical documents, uses full gray scale information, does not require binarization as pre-processing or prior segmentation of individual text lines. We evaluate its performance using established error metrics, previously used in competitions for word segmentation, and demonstrate its usefulness for a 15th century handwritten document.

  • 4.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Experiments on Large Scale Document Visualization using Image-based Word Clouds2015Report (Other academic)
    Abstract [en]

    In this paper, we introduce image-based word clouds as a novel tool for a quick and aesthetic overviews of common words in collections of digitized text manuscripts. While OCR can be used to enable summaries and search functionality to printed modern text, historical and handwritten documents remains a challenge. By segmenting and counting word images, without applying manual transcription or OCR, we have developed a method that can produce word- or tag clouds from document collections. Our new tool is not limited to any specific kind of text. We make further contributions in ways of stop-word removal, class based feature weighting and visualization. An evaluation of the proposed tool includes comparisons with ground truth word clouds on handwritten marriage licenses from the 17th century and the George Washington database of handwritten letters, from the 18th century. Our experiments show that image-based word clouds capture the same information, albeit approximately, as the regular word clouds based on text data.

  • 5.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Visualizing document image collections using image-based word clouds2015In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I / [ed] Bebis, G; Boyle, R; Parvin, B; Koracin, D; Pavlidis, I; Feris, R; McGraw, T; Elendt, M; Kopper, R; Ragan, E; Ye, Z; Weber, G, Springer, 2015, p. 297-306Conference paper (Refereed)
  • 6.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindström, Jonas
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of History.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections2017Conference paper (Other academic)
  • 7.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindström, Jonas
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of History.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Neural Ctrl-F: Segmentation-free query-by-string word spotting in handwritten manuscript collections2017In: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, p. 4443-4452Conference paper (Refereed)
    Abstract [en]

    In this paper, we approach the problem of segmentation-free query-by-string word spotting for handwritten documents. In other words, we use methods inspired from computer vision and machine learning to search for words in large collections of digitized manuscripts. In particular, we are interested in historical handwritten texts, which are often far more challenging than modern printed documents. This task is important, as it provides people with a way to quickly find what they are looking for in large collections that are tedious and difficult to read manually. To this end, we introduce an end-to-end trainable model based on deep neural networks that we call Ctrl-F-Net. Given a full manuscript page, the model simultaneously generates region proposals, and embeds these into a distributed word embedding space, where searches are performed. We evaluate the model on common benchmarks for handwritten word spotting, outperforming the previous state-of-the-art segmentation-free approaches by a large margin, and in some cases even segmentation-based approaches. One interesting real-life application of our approach is to help historians to find and count specific words in court records that are related to women's sustenance activities and division of labor. We provide promising preliminary experiments that validate our method on this task.

  • 8.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Lindström, Jonas
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Arts, Department of History.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Neural Word Search in Historical Manuscript CollectionsManuscript (preprint) (Other academic)
    Abstract [en]

    We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first propose an end-to-end trainable model based on deep neural networks that we dub Ctrl-F-Net. The model simultaneously generates region proposals and embeds them into a word embedding space, wherein a search is performed. We further introduce a simplified version called Ctrl-F-Mini. It is faster with similar performance, though it is limited to more easily segmented manuscripts. We evaluate both models on common benchmark datasets and surpass the previous state of the art. Finally, in collaboration with historians, we employ the Ctrl-F-Net to search within a large manuscript collection of over 100 thousand pages, written across two centuries. With only 11 training pages, we enable large scale data collection in manuscript-based historical research. This results in a speed up of data collection and the number of manuscripts processed by orders of magnitude. Given the time consuming manual work required to study old manuscripts in the humanities, quick and robust tools for word spotting has the potential to revolutionise domains like history, religion and language.

  • 9.
    Wilkinson, Tomas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Nettelblad, Carl
    Bootstrapping Weakly Supervised Segmentation-free Word Spotting through HMM-based AlignmentManuscript (preprint) (Other academic)
    Abstract [en]

    Recent work in word spotting in handwritten documents has yielded impressive results. Yet this progress has largely been made by supervised learning systems which are dependant on manually annotated data, making deployment to new collections a significant effort. In this paper we propose an approach utilising transcriptions without bounding box annotations to train segmentation-free word spotting models, given a model partially trained with full annotations. This is done through an alignment procedure based on hidden Markov models. This model can create a tentative mapping between word region proposals and the transcriptions to automatically create additional weakly annotated training data. Using as little as 1% and 10% of the fully annotated training sets for partial convergence, we automatically annotate the remaining training data and successfully train using it. Across all datasets, our approach comes within a few mAP% of achieving the same performance as a model trained with only full ground truth. We believe that this will be a significant advance towards a more general use of word spotting, since digital transcription data will already exist for parts of many collections of interest.

1 - 9 of 9
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