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Author Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features Type Conference Article
  Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.  
  Address Aspen; Colorado; USA; March 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ MDB2020 Serial 3334  
Permanent link to this record
 

 
Author Arnau Baro; Alicia Fornes; Carles Badal edit   pdf
openurl 
  Title Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism Type Conference Article
  Year 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks.  
  Address Virtual ICFHR; September 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ BFB2020 Serial 3448  
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Author Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora edit  url
isbn  openurl
  Title Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images Type Book Chapter
  Year 2020 Publication Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher World Scientific Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-981-120-323-7 Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ FLP2020 Serial 3350  
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Author Juan Ignacio Toledo edit  isbn
openurl 
  Title Information Extraction from Heterogeneous Handwritten Documents Type Book Whole
  Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract In this thesis we explore information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes roughly equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words.
In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
 
  Address July 2019  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Alicia Fornes;Josep Llados  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-948531-7-3 Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ Tol2019 Serial 3389  
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Author Albert Berenguel edit  isbn
openurl 
  Title Analysis of background textures in banknotes and identity documents for counterfeit detection Type Book Whole
  Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract Counterfeiting and piracy are a form of theft that has been steadily growing in recent years. A counterfeit is an unauthorized reproduction of an authentic/genuine object. Banknotes and identity documents are two common objects of counterfeiting. The former is used by organized criminal groups to finance a variety of illegal activities or even to destabilize entire countries due the inflation effect. Generally, in order to run their illicit businesses, counterfeiters establish companies and bank accounts using fraudulent identity documents. The illegal activities generated by counterfeit banknotes and identity documents has a damaging effect on business, the economy and the general population. To fight against counterfeiters, governments and authorities around the globe cooperate and develop security features to protect their security documents. Many of the security features in identity documents can also be found in banknotes. In this dissertation we focus our efforts in detecting the counterfeit banknotes and identity documents by analyzing the security features at the background printing. Background areas on secure documents contain fine-line patterns and designs that are difficult to reproduce without the manufacturers cutting-edge printing equipment. Our objective is to find the loose of resolution between the genuine security document and the printed counterfeit version with a publicly available commercial printer. We first present the most complete survey to date in identity and banknote security features. The compared algorithms and systems are based on computer vision and machine learning. Then we advance to present the banknote and identity counterfeit dataset we have built and use along all this thesis. Afterwards, we evaluate and adapt algorithms in the literature for the security background texture analysis. We study this problem from the point of view of robustness, computational efficiency and applicability into a real and non-controlled industrial scenario, proposing key insights to use these algorithms. Next, within the industrial environment of this thesis, we build a complete service oriented architecture to detect counterfeit documents. The mobile application and the server framework intends to be used even by non-expert document examiners to spot counterfeits. Later, we re-frame the problem of background texture counterfeit detection as a full-reference game of spotting the differences, by alternating glimpses between a counterfeit and a genuine background using recurrent neural networks. Finally, we deal with the lack of counterfeit samples, studying different approaches based on anomaly detection.  
  Address November 2019  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Oriol Ramos Terrades;Josep Llados  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-121011-2-6 Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ Ber2019 Serial 3395  
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Author Sangeeth Reddy; Minesh Mathew; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar edit   pdf
openurl 
  Title RoadText-1K: Text Detection and Recognition Dataset for Driving Videos Type Conference Article
  Year 2020 Publication IEEE International Conference on Robotics and Automation Abbreviated Journal  
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  Abstract Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new ”RoadText-1K” dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results signify the challenges in unconstrained driving videos compared to existing datasets. This suggests that RoadText-1K is suited for research and development of reading systems, robust enough to be incorporated into more complex downstream tasks like driver assistance and self-driving. The dataset can be found at http://cvit.iiit.ac.in/research/
projects/cvit-projects/roadtext-1k
 
  Address Paris; Francia; ???  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICRA  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ RMG2020 Serial 3400  
Permanent link to this record
 

 
Author Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas edit   pdf
openurl 
  Title Location Sensitive Image Retrieval and Tagging Type Conference Article
  Year 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  Abstract People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.  
  Address Virtual; August 2020  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GGG2020b Serial 3420  
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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 2020 Publication 17th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.129; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ KRR2020 Serial 3425  
Permanent link to this record
 

 
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 2020 Publication 16th European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages (up)  
  Keywords  
  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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; 600.140; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KPW2020 Serial 3426  
Permanent link to this record
 

 
Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) edit  doi
isbn  openurl
  Title 16th International Conference, 2021, Proceedings, Part III Type Book Whole
  Year 2021 Publication Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal  
  Volume 12823 Issue Pages (up)  
  Keywords  
  Abstract This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.

The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
 
  Address Lausanne, Switzerland, September 5-10, 2021  
  Corporate Author Thesis  
  Publisher Springer Cham Place of Publication Editor Josep Llados; Daniel Lopresti; Seiichi Uchida  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-030-86333-3 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ Serial 3727  
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