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Author Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl edit  url
openurl 
  Title Decryption of historical manuscripts: the DECRYPT project Type Journal Article
  Year (down) 2020 Publication Cryptologia Abbreviated Journal CRYPT  
  Volume 44 Issue 6 Pages 545-559  
  Keywords automatic decryption; cipher collection; historical cryptology; image transcription  
  Abstract Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.  
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  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ MEF2020 Serial 3347  
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez edit   pdf
openurl 
  Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Conference Article
  Year (down) 2020 Publication Women in Geometry and Topology Abbreviated Journal  
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  Address Barcelona; September 2019  
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  Notes IAM; DAG; 600.139; 600.145; 600.121 Approved no  
  Call Number Admin @ si @ GRP2020 Serial 3473  
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Author Fernando Vilariño edit  openurl
  Title Unveiling the Social Impact of AI Type Conference Article
  Year (down) 2020 Publication Workshop at Digital Living Lab Days Conference Abbreviated Journal  
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  Address September 2020  
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  Notes MV; DAG; 600.121; 600.140;SIAI Approved no  
  Call Number Admin @ si @ Vil2020 Serial 3459  
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Author Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi edit   pdf
openurl 
  Title A Web-based Interactive Transcription Tool for Encrypted Manuscripts Type Conference Article
  Year (down) 2020 Publication 3rd International Conference on Historical Cryptology Abbreviated Journal  
  Volume Issue Pages 52-59  
  Keywords  
  Abstract Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available.
 
  Address Virtual; June 2020  
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  ISSN ISBN Medium  
  Area Expedition Conference HistoCrypt  
  Notes DAG; 600.140; 602.230; 600.121 Approved no  
  Call Number Admin @ si @ CSF2020 Serial 3447  
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Author Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas edit  openurl
  Title WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval Type Book Chapter
  Year (down) 2020 Publication Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis Abbreviated Journal  
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  Publisher Springer Place of Publication Editor Analysis”, K. Alahari; C.V. Jawahar  
  Language Summary Language Original Title  
  Series Editor Series Title Series on Advances in Computer Vision and Pattern Recognition Abbreviated Series Title  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ AGG2020 Serial 3496  
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Author Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Text Recognition – Real World Data and Where to Find Them Type Conference Article
  Year (down) 2020 Publication 25th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 4489-4496  
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  Abstract We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya.  
  Address Virtual; January 2021  
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  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ JMG2020 Serial 3557  
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Author Lei Kang edit  isbn
openurl 
  Title Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation Type Book Whole
  Year (down) 2020 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
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  Abstract Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded.
In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance.
We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future.
 
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Alicia Fornes;Marçal Rusiñol;Mauricio Villegas  
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  ISSN ISBN 978-84-122714-0-9 Medium  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ Kan20 Serial 3482  
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Author Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas edit   pdf
url  doi
openurl 
  Title Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition Type Conference Article
  Year (down) 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.  
  Address Aspen; Colorado; USA; March 2020  
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  Area Expedition Conference WACV  
  Notes DAG; 600.129; 600.140; 601.302; 601.312; 600.121 Approved no  
  Call Number Admin @ si @ KRF2020 Serial 3446  
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Author Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas edit   pdf
openurl 
  Title Distilling Content from Style for Handwritten Word Recognition Type Conference Article
  Year (down) 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Despite the latest transcription accuracies reached using deep neural network architectures, handwritten text recognition still remains a challenging problem, mainly because of the large inter-writer style variability. Both augmenting the training set with artificial samples using synthetic fonts, and writer adaptation techniques have been proposed to yield more generic approaches aimed at dodging style unevenness. In this work, we take a step closer to learn style independent features from handwritten word images. We propose a novel method that is able to disentangle the content and style aspects of input images by jointly optimizing a generative process and a handwritten
word recognizer. The generator is aimed at transferring writing style features from one sample to another in an image-to-image translation approach, thus leading to a learned content-centric features that shall be independent to writing style attributes.
Our proposed recognition model is able then to leverage such writer-agnostic features to reach better recognition performances. We advance over prior training strategies and demonstrate with qualitative and quantitative evaluations the performance of both
the generative process and the recognition efficiency in the IAM dataset.
 
  Address Virtual ICFHR; September 2020  
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  Area Expedition Conference ICFHR  
  Notes DAG; 600.129; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ KRR2020a Serial 3425  
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Author Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas edit   pdf
openurl 
  Title GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images Type Conference Article
  Year (down) 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal  
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  Abstract Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images.  
  Address Virtual; August 2020  
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  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; 600.140; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KPW2020 Serial 3426  
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