toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
Author Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados edit  url
doi  openurl
  Title (down) TWD: A New Deep E2E Model for Text Watermark Detection in Video Images Type Conference Article
  Year 2022 Publication 26th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection  
  Abstract Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge  
  Address Montreal; Quebec; Canada; August 2022  
  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 ICPR  
  Notes DAG; Approved no  
  Call Number Admin @ si @ BSA2022 Serial 3788  
Permanent link to this record
 

 
Author Souhail Bakkali; Sanket Biswas; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades; Josep Llados edit   pdf
url  openurl
  Title (down) TransferDoc: A Self-Supervised Transferable Document Representation Learning Model Unifying Vision and Language Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The field of visual document understanding has witnessed a rapid growth in emerging challenges and powerful multi-modal strategies. However, they rely on an extensive amount of document data to learn their pretext objectives in a ``pre-train-then-fine-tune'' paradigm and thus, suffer a significant performance drop in real-world online industrial settings. One major reason is the over-reliance on OCR engines to extract local positional information within a document page. Therefore, this hinders the model's generalizability, flexibility and robustness due to the lack of capturing global information within a document image. We introduce TransferDoc, a cross-modal transformer-based architecture pre-trained in a self-supervised fashion using three novel pretext objectives. TransferDoc learns richer semantic concepts by unifying language and visual representations, which enables the production of more transferable models. Besides, two novel downstream tasks have been introduced for a ``closer-to-real'' industrial evaluation scenario where TransferDoc outperforms other state-of-the-art approaches.  
  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 @ BBM2023 Serial 3995  
Permanent link to this record
 

 
Author Francesc Net; Marc Folia; Pep Casals; Lluis Gomez edit  url
openurl 
  Title (down) Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections Type Conference Article
  Year 2023 Publication 17th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 14191 Issue Pages 3-17  
  Keywords Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning  
  Abstract This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset.  
  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 LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG Approved no  
  Call Number Admin @ si @ NFC2023 Serial 3859  
Permanent link to this record
 

 
Author Alicia Fornes; Beata Megyesi; Joan Mas edit   pdf
openurl 
  Title (down) Transcription of Encoded Manuscripts with Image Processing Techniques Type Conference Article
  Year 2017 Publication Digital Humanities Conference Abbreviated Journal  
  Volume Issue Pages 441-443  
  Keywords  
  Abstract  
  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 DH  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ FMM2017 Serial 3061  
Permanent link to this record
 

 
Author Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke edit  doi
openurl 
  Title (down) Transcription Alignment of Latin Manuscripts Using Hidden Markov Models Type Conference Article
  Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal  
  Volume Issue Pages 29-36  
  Keywords  
  Abstract Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.  
  Address  
  Corporate Author Thesis  
  Publisher ACM 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 @ FFF2011b Serial 1824  
Permanent link to this record
 

 
Author Ekta Vats; Anders Hast; Alicia Fornes edit   pdf
url  doi
openurl 
  Title (down) Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion Type Conference Article
  Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1294-1299  
  Keywords Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching  
  Abstract Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
 
  Address Sydney; Australia; September 2019  
  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; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ VHF2019 Serial 3356  
Permanent link to this record
 

 
Author Arnau Baro; Pau Riba; Alicia Fornes edit   pdf
doi  openurl
  Title (down) Towards the recognition of compound music notes in handwritten music scores Type Conference Article
  Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising.  
  Address Shenzhen; China; October 2016  
  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 2167-6445 ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ BRF2016 Serial 2903  
Permanent link to this record
 

 
Author Pau Riba; Alicia Fornes; Josep Llados edit  isbn
openurl 
  Title (down) Towards the Alignment of Handwritten Music Scores Type Conference Article
  Year 2015 Publication 11th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.  
  Address Nancy; France; August 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor Bart Lamiroy; Rafael Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-52158-9 Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number Admin @ si @ Serial 2874  
Permanent link to this record
 

 
Author Pau Riba; Alicia Fornes; Josep Llados edit   pdf
url  isbn
openurl 
  Title (down) Towards the Alignment of Handwritten Music Scores Type Book Chapter
  Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages 103-116  
  Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment  
  Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-52158-9 Medium  
  Area Expedition Conference  
  Notes DAG; 600.097; 602.006; 600.121 Approved no  
  Call Number Admin @ si @ RFL2017 Serial 2955  
Permanent link to this record
 

 
Author Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer edit   pdf
url  openurl
  Title (down) Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. Type Conference Article
  Year 2020 Publication International Workshop on Artificial Intelligence for Healthcare Applications Abbreviated Journal  
  Volume 12661 Issue Pages 476-489  
  Keywords  
  Abstract Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.  
  Address Virtual; January 2021  
  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 ICPRW  
  Notes DAG; 600.121; 600.140; Approved no  
  Call Number Admin @ si @ BCF2020 Serial 3508  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: