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Author Sergi Garcia Bordils; George Tom; Sangeeth Reddy; Minesh Mathew; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas edit   pdf
url  doi
isbn  openurl
  Title Read While You Drive-Multilingual Text Tracking on the Road Type Conference Article
  Year 2022 Publication 15th IAPR International workshop on document analysis systems Abbreviated Journal  
  Volume 13237 Issue Pages 756–770  
  Keywords (up)  
  Abstract Visual data obtained during driving scenarios usually contain large amounts of text that conveys semantic information necessary to analyse the urban environment and is integral to the traffic control plan. Yet, research on autonomous driving or driver assistance systems typically ignores this information. To advance research in this direction, we present RoadText-3K, a large driving video dataset with fully annotated text. RoadText-3K is three times bigger than its predecessor and contains data from varied geographical locations, unconstrained driving conditions and multiple languages and scripts. We offer a comprehensive analysis of tracking by detection and detection by tracking methods exploring the limits of state-of-the-art text detection. Finally, we propose a new end-to-end trainable tracking model that yields state-of-the-art results on this challenging dataset. Our experiments demonstrate the complexity and variability of RoadText-3K and establish a new, realistic benchmark for scene text tracking in the wild.  
  Address La Rochelle; France; May 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-06554-5 Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.155; 611.022; 611.004 Approved no  
  Call Number Admin @ si @ GTR2022 Serial 3783  
Permanent link to this record
 

 
Author Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai edit   pdf
url  doi
isbn  openurl
  Title Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks Type Conference Article
  Year 2022 Publication 17th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 329–344  
  Keywords (up)  
  Abstract Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-25068-2 Medium  
  Area Expedition Conference ECCV-TiE  
  Notes DAG; 600.162; 600.140; 110.312 Approved no  
  Call Number Admin @ si @ GBC2022 Serial 3795  
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Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) edit  doi
isbn  openurl
  Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
  Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal  
  Volume 13639 Issue Pages  
  Keywords (up)  
  Abstract  
  Address ICFHR 2022, Hyderabad, India, December 4–7, 2022  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-21648-0 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ PFS2022 Serial 3809  
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Author Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados edit  url
openurl 
  Title Classification of aesthetic natural scene images using statistical and semantic features Type Journal Article
  Year 2023 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 82 Issue 9 Pages 13507-13532  
  Keywords (up)  
  Abstract Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images.  
  Address  
  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  
  Notes DAG Approved no  
  Call Number Admin @ si @ BSP2023 Serial 3873  
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Author Asma Bensalah; Antonio Parziale; Giuseppe De Gregorio; Angelo Marcelli; Alicia Fornes; Josep Llados edit  url
doi  openurl
  Title I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation Type Conference Article
  Year 2023 Publication 21st International Graphonomics Conference Abbreviated Journal  
  Volume Issue Pages 136–148  
  Keywords (up)  
  Abstract During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.  
  Address Evora; Portugal; October 2023  
  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 IGS  
  Notes DAG Approved no  
  Call Number Admin @ si @ BPG2023 Serial 3838  
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Author Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas edit   pdf
url  openurl
  Title OCR-IDL: OCR Annotations for Industry Document Library Dataset Type Conference Article
  Year 2022 Publication ECCV Workshop on Text in Everything Abbreviated Journal  
  Volume Issue Pages  
  Keywords (up)  
  Abstract Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL.  
  Address  
  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; no proj Approved no  
  Call Number Admin @ si @ BTG2022 Serial 3817  
Permanent link to this record
 

 
Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny edit   pdf
doi  openurl
  Title Hierarchical multimodal transformers for Multi-Page DocVQA Type Journal Article
  Year 2023 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 144 Issue Pages 109834  
  Keywords (up)  
  Abstract Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that should be processed altogether. In this work we extend DocVQA to the multi-page scenario. For that, we first create a new dataset, MP-DocVQA, where questions are posed over multi-page documents instead of single pages. Second, we propose a new hierarchical method, Hi-VT5, based on the T5 architecture, that overcomes the limitations of current methods to process long multi-page documents. The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer. Through extensive experimentation, we demonstrate that our method is able, in a single stage, to answer the questions and provide the page that contains the relevant information to find the answer, which can be used as a kind of explainability measure.  
  Address  
  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 ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.155; 600.121 Approved no  
  Call Number Admin @ si @ TKV2023 Serial 3825  
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Author Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades edit   pdf
doi  openurl
  Title VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification Type Journal Article
  Year 2023 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 139 Issue Pages 109419  
  Keywords (up)  
  Abstract Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets.  
  Address  
  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 ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ BMC2023 Serial 3826  
Permanent link to this record
 

 
Author Ruben Tito; Dimosthenis Karatzas; Ernest Valveny edit   pdf
url  openurl
  Title Hierarchical multimodal transformers for Multipage DocVQA Type Journal Article
  Year 2023 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 144 Issue 109834 Pages  
  Keywords (up)  
  Abstract Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure.  
  Address  
  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  
  Notes DAG Approved no  
  Call Number Admin @ si @ TKV2023 Serial 3836  
Permanent link to this record
 

 
Author Mohamed Ali Souibgui; Pau Torras; Jialuo Chen; Alicia Fornes edit  url
openurl 
  Title An Evaluation of Handwritten Text Recognition Methods for Historical Ciphered Manuscripts Type Conference Article
  Year 2023 Publication 7th International Workshop on Historical Document Imaging and Processing Abbreviated Journal  
  Volume Issue Pages 7-12  
  Keywords (up)  
  Abstract This paper investigates the effectiveness of different deep learning HTR families, including LSTM, Seq2Seq, and transformer-based approaches with self-supervised pretraining, in recognizing ciphered manuscripts from different historical periods and cultures. The goal is to identify the most suitable method or training techniques for recognizing ciphered manuscripts and to provide insights into the challenges and opportunities in this field of research. We evaluate the performance of these models on several datasets of ciphered manuscripts and discuss their results. This study contributes to the development of more accurate and efficient methods for recognizing historical manuscripts for the preservation and dissemination of our cultural heritage.  
  Address  
  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 HIP  
  Notes DAG Approved no  
  Call Number Admin @ si @ STC2023 Serial 3849  
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