toggle visibility Search & Display Options
Select All    Deselect All << 1 2 3 4 5 6 7 8 9 10 >> [11–20]
List View
 | 
Citations
 | 
Details
  Records Links (down)
Author Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes edit  url
openurl 
  Title Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images Type Conference Article
  Year 2023 Publication Document Analysis and Recognition – ICDAR 2023 Workshops Abbreviated Journal  
  Volume 14193 Issue Pages 83-93  
  Keywords Historical Manuscripts; Symbol Alignment  
  Abstract Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.  
  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 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ TSS2023 Serial 3850  
Permanent link to this record
 
 
Author Adarsh Tiwari; Sanket Biswas; Josep Llados edit  url
openurl 
  Title Can Pre-trained Language Models Help in Understanding Handwritten Symbols? Type Conference Article
  Year 2023 Publication 17th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 14193 Issue Pages 199–211  
  Keywords  
  Abstract The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.  
  Address San Jose; CA; USA; August 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 ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ TBL2023 Serial 3908  
Permanent link to this record
 
 
Author Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Out-of-Vocabulary Challenge Report Type Conference Article
  Year 2022 Publication Proceedings European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 359–375  
  Keywords  
  Abstract This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.  
  Address Tel-Aviv; Israel; October 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 Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.155; 302.105; 611.002 Approved no  
  Call Number Admin @ si @ GMB2022 Serial 3771  
Permanent link to this record
 
 
Author Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez edit   pdf
url  doi
openurl 
  Title MUST-VQA: MUltilingual Scene-text VQA Type Conference Article
  Year 2022 Publication Proceedings European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 345–358  
  Keywords Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models  
  Abstract In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.  
  Address Tel-Aviv; Israel; October 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 Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 302.105; 600.155; 611.002 Approved no  
  Call Number Admin @ si @ VBM2022 Serial 3770  
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  
  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  
Permanent link to this record
 
 
Author Alicia Fornes; Asma Bensalah; Cristina Carmona_Duarte; Jialuo Chen; Miguel A. Ferrer; Andreas Fischer; Josep Llados; Cristina Martin; Eloy Opisso; Rejean Plamondon; Anna Scius-Bertrand; Josep Maria Tormos edit   pdf
url  doi
openurl 
  Title The RPM3D Project: 3D Kinematics for Remote Patient Monitoring Type Conference Article
  Year 2022 Publication Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 Abbreviated Journal  
  Volume 13424 Issue Pages 217-226  
  Keywords Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics  
  Abstract This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.  
  Address June 7-9, 2022, Las Palmas de Gran Canaria, Spain  
  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 Medium  
  Area Expedition Conference IGS  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ FBC2022 Serial 3739  
Permanent link to this record
 
 
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  
  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 Ruben Tito; Dimosthenis Karatzas; Ernest Valveny edit   pdf
url  openurl
  Title Document Collection Visual Question Answering Type Conference Article
  Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 12822 Issue Pages 778-792  
  Keywords Document collection; Visual Question Answering  
  Abstract Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.  
  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 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ TKV2021 Serial 3622  
Permanent link to this record
 
 
Author Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados edit  url
openurl 
  Title A Self-supervised Inverse Graphics Approach for Sketch Parametrization Type Conference Article
  Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 12916 Issue Pages 28-42  
  Keywords  
  Abstract The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.  
  Address Lausanne; Suissa; September 2021  
  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 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ SRR2021 Serial 3675  
Permanent link to this record
 
 
Author Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes edit  url
openurl 
  Title A Transcription Is All You Need: Learning to Align through Attention Type Conference Article
  Year 2021 Publication 14th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume 12916 Issue Pages 141–146  
  Keywords  
  Abstract Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.  
  Address Virtual; September 2021  
  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 Medium  
  Area Expedition Conference GREC  
  Notes DAG; 602.230; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ TSC2021 Serial 3619  
Permanent link to this record
Select All    Deselect All << 1 2 3 4 5 6 7 8 9 10 >> [11–20]
List View
 | 
Citations
 | 
Details
toggle visibility
Save Citations:
Export Records: