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Author Pau Riba; Alicia Fornes; Josep Llados
Title 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 (up) DAG Approved no
Call Number Admin @ si @ Serial 2874
Permanent link to this record
 
 
Author Lluis Gomez
Title Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding Type Book Whole
Year 2016 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Dimosthenis Karatzas
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ Gom2016 Serial 2891
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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
Title Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary Type Book Chapter
Year 2016 Publication Recent Trends in Image Processing and Pattern Recognition Abbreviated Journal
Volume 709 Issue Pages
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 RTIP2R
Notes (up) DAG Approved no
Call Number Admin @ si @ HTR2016 Serial 2956
Permanent link to this record
 
 
Author Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez
Title Computer Vision in Vehicle Technology: Land, Sea & Air Type Book Whole
Year Publication Computer Vision in Vehicle Technology: Land, Sea & Air Abbreviated Journal
Volume Issue Pages
Keywords
Abstract A unified view of the use of computer vision technology for different types of vehicles

Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).

The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed.
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 978-1-118-86807-2 Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ LIP2017b Serial 3049
Permanent link to this record
 
 
Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
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
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 (up) DAG Approved no
Call Number Admin @ si @ Serial 3727
Permanent link to this record
 
 
Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
Title 16th International Conference, 2021, Proceedings, Part IV Type Book Whole
Year 2021 Publication Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal
Volume 12824 Issue Pages
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 978-3-030-86336-4 Medium
Area Expedition Conference ICDAR
Notes (up) DAG Approved no
Call Number Admin @ si @ Serial 3728
Permanent link to this record
 
 
Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
Title 16th International Conference, 2021, Proceedings, Part I Type Book Whole
Year 2021 Publication Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal
Volume 12821 Issue Pages
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: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition.
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 978-3-030-86548-1 Medium
Area Expedition Conference ICDAR
Notes (up) DAG Approved no
Call Number Admin @ si @ Serial 3725
Permanent link to this record
 
 
Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
Title 16th International Conference, 2021, Proceedings, Part II Type Book Whole
Year 2021 Publication Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal
Volume 12822 Issue Pages
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 978-3-030-86330-2 Medium
Area Expedition Conference ICDAR
Notes (up) DAG Approved no
Call Number Admin @ si @ Serial 3726
Permanent link to this record
 
 
Author Josep Llados
Title The 5G of Document Intelligence Type Conference Article
Year 2021 Publication 3rd Workshop on Future of Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference FDAR
Notes (up) DAG Approved no
Call Number Admin @ si @ Serial 3677
Permanent link to this record
 
 
Author Ali Furkan Biten
Title A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA).
In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Dimosthenis Karatzas;Lluis Gomez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-5-5 Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ Bit2022 Serial 3755
Permanent link to this record
 
 
Author Andres Mafla
Title Leveraging Scene Text Information for Image Interpretation Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Until recently, most computer vision models remained illiterate, largely ignoring the semantically rich and explicit information contained in scene text. Recent progress in scene text detection and recognition has recently allowed exploring its role in a diverse set of open computer vision problems, e.g. image classification, image-text retrieval, image captioning, and visual question answering to name a few. The explicit semantics of scene text closely requires specific modeling similar to language. However, scene text is a particular signal that has to be interpreted according to a comprehensive perspective that encapsulates all the visual cues in an image. Incorporating this information is a straightforward task for humans, but if we are unfamiliar with a language or scripture, achieving a complete world understanding is impossible (e.a. visiting a foreign country with a different alphabet). Despite the importance of scene text, modeling it requires considering the several ways in which scene text interacts with an image, processing and fusing an additional modality. In this thesis, we mainly focus
on two tasks, scene text-based fine-grained image classification, and cross-modal retrieval. In both studied tasks we identify existing limitations in current approaches and propose plausible solutions. Concretely, in each chapter: i) We define a compact way to embed scene text that generalizes to unseen words at training time while performing in real-time. ii) We incorporate the previously learned scene text embedding to create an image-level descriptor that overcomes optical character recognition (OCR) errors which is well-suited to the fine-grained image classification task. iii) We design a region-level reasoning network that learns the interaction through semantics among salient visual regions and scene text instances. iv) We employ scene text information in image-text matching and introduce the Scene Text Aware Cross-Modal retrieval StacMR task. We gather a dataset that incorporates scene text and design a model suited for the newly studied modality. v) We identify the drawbacks of current retrieval metrics in cross-modal retrieval. An image captioning metric is proposed as a way of better evaluating semantics in retrieved results. Ample experimentation shows that incorporating such semantics into a model yields better semantic results while
requiring significantly less data to converge.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Dimosthenis Karatzas;Lluis Gomez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-6-2 Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ Maf2022 Serial 3756
Permanent link to this record
 
 
Author Mohamed Ali Souibgui
Title Document Image Enhancement and Recognition in Low Resource Scenarios: Application to Ciphers and Handwritten Text Type Book Whole
Year 2022 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this thesis, we propose different contributions with the goal of enhancing and recognizing historical handwritten document images, especially the ones with rare scripts, such as cipher documents.
In the first part, some effective end-to-end models for Document Image Enhancement (DIE) using deep learning models were presented. First, Generative Adversarial Networks (cGAN) for different tasks (document clean-up, binarization, deblurring, and watermark removal) were explored. Next, we further improve the results by recovering the degraded document images into a clean and readable form by integrating a text recognizer into the cGAN model to promote the generated document image to be more readable. Afterward, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion.
The second part of the thesis addresses Handwritten Text Recognition (HTR) in low resource scenarios, i.e. when only few labeled training data is available. We propose novel methods for recognizing ciphers with rare scripts. First, a few-shot object detection based method was proposed. Then, we incorporate a progressive learning strategy that automatically assignspseudo-labels to a set of unlabeled data to reduce the human labor of annotating few pages while maintaining the good performance of the model. Secondly, a data generation technique based on Bayesian Program Learning (BPL) is proposed to overcome the lack of data in such rare scripts. Thirdly, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE). This latter self-supervised model is designed to tackle two tasks, text recognition and document image enhancement. The proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time, it requires substantially fewer data samples to converge.
In the third part of the thesis, we analyze, from the user perspective, the usage of HTR systems in low resource scenarios. This contrasts with the usual research on HTR, which often focuses on technical aspects only and rarely devotes efforts on implementing software tools for scholars in Humanities.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIMA Place of Publication Editor Alicia Fornes;Yousri Kessentini
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-124793-8-6 Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number Admin @ si @ Sou2022 Serial 3757
Permanent link to this record
 
 
Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds)
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
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 (up) DAG Approved no
Call Number Admin @ si @ PFS2022 Serial 3809
Permanent link to this record
 
 
Author Kunal Biswas; Palaiahnakote Shivakumara; Umapada Pal; Tong Lu; Michel Blumenstein; Josep Llados
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
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 (up) 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
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
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 (up) DAG Approved no
Call Number Admin @ si @ BPG2023 Serial 3838
Permanent link to this record