| |
Records |
Links  |
|
Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
|
| |
Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
Type |
Journal |
| |
Year |
2014 |
Publication |
Manuscript Cultures |
Abbreviated Journal |
|
|
| |
Volume |
7 |
Issue |
|
Pages |
47-58 |
|
| |
Keywords |
|
|
| |
Abstract |
With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
|
| |
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; 600.061; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3190 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; M. Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
|
| |
Title |
ICDAR 2019 Competition on Scene Text Visual Question Answering |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios.
The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that
can exploit scene text to achieve holistic image understanding. |
|
| |
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 |
CLVL |
|
| |
Notes |
DAG; 600.129; 601.338; 600.135; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BTM2019a |
Serial |
3284 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Riba; Alicia Fornes; Josep Llados |
|
| |
Title |
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 |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
|
| |
Title |
Ontology-Based Understanding of Architectural Drawings |
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 |
75-85 |
|
| |
Keywords |
Graphics recognition; Floor plan analysi; Domain ontology |
|
| |
Abstract |
In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
|
| |
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 |
|
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HRL2017 |
Serial |
3086 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
|
| |
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 |
|
| |
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 |
|
| |
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 |
| |
|
| |
|
Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
|
| |
Title |
Graph-Based Deep Generative Modelling for Document Layout Generation |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
12917 |
Issue |
|
Pages |
525-537 |
|
| |
Keywords |
|
|
| |
Abstract |
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices. |
|
| |
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 |
|
|
| |
Notes |
DAG; 600.121; 600.140; 110.312 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRL2021 |
Serial |
3676 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Anguelos Nicolaou; Sounak Dey; V.Christlein; A.Maier; Dimosthenis Karatzas |
|
| |
Title |
Non-deterministic Behavior of Ranking-based Metrics when Evaluating Embeddings |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
International Workshop on Reproducible Research in Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
11455 |
Issue |
|
Pages |
71-82 |
|
| |
Keywords |
|
|
| |
Abstract |
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits. |
|
| |
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 |
|
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ NDC2018 |
Serial |
3178 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
|
| |
Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
11134 |
Issue |
|
Pages |
530-544 |
|
| |
Keywords |
|
|
| |
Abstract |
Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. |
|
| |
Address |
Munich; Alemanya; September 2018 |
|
| |
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; 600.129; 601.338; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2018b |
Serial |
3176 |
|
| Permanent link to this record |
