| Records |
| Author |
Gemma Sanchez; Josep Llados; K. Tombre |
| Title |
A mean string algorithm to compute the average among a set of 2D shapes |
Type |
Journal Article |
| Year |
2002 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
23 |
Issue |
1-3 |
Pages |
203–214 |
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Notes  |
DAG; IF: 0.409 |
Approved |
no |
| Call Number |
DAG @ dag @ SLT2002 |
Serial |
275 |
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| Author |
Ernest Valveny; Enric Marti |
| Title |
A model for image generation and symbol recognition through the deformation of lineal shapes |
Type |
Journal Article |
| Year |
2003 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
24 |
Issue |
15 |
Pages |
2857-2867 |
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| Abstract |
We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
Place of Publication |
New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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no |
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IAM @ iam @ VAM2003 |
Serial |
1653 |
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| Author |
Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados |
| Title |
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
136 |
Issue |
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Pages |
219-227 |
| Keywords |
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| Abstract |
In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. |
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| Notes |
DAG; 600.140; 601.311; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ CFV2020 |
Serial |
3451 |
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| Author |
B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols |
| Title |
Knowledge graph based methods for record linkage |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
136 |
Issue |
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Pages |
127-133 |
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| Abstract |
Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results. |
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| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ GRP2020 |
Serial |
3453 |
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| Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
| Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
Type |
Journal Article |
| Year |
2022 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
160 |
Issue |
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Pages |
43-49 |
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| Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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| Notes |
DAG; 600.121; 600.162; 602.230 |
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no |
| Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
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| Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
| Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
Type |
Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
149 |
Issue |
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Pages |
164-171 |
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| Abstract |
Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ DGV2021 |
Serial |
3364 |
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| Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
| Title |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
Type |
Journal Article |
| Year |
2017 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
87 |
Issue |
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Pages |
203-211 |
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| Abstract |
Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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| Notes |
DAG; 600.097; 602.006; 603.053; 600.121 |
Approved |
no |
| Call Number |
RLF2017b |
Serial |
2873 |
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| Author |
Pau Riba; Josep Llados; Alicia Fornes |
| Title |
Hierarchical graphs for coarse-to-fine error tolerant matching |
Type |
Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
134 |
Issue |
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Pages |
116-124 |
| Keywords |
Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval |
| Abstract |
During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). |
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DAG; 600.097; 601.302; 603.057; 600.140; 600.121 |
Approved |
no |
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Admin @ si @ RLF2020 |
Serial |
3349 |
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| Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
| Title |
From Optical Music Recognition to Handwritten Music Recognition: a Baseline |
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Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
123 |
Issue |
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Pages |
1-8 |
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Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community. |
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| Notes |
DAG; 600.097; 601.302; 601.330; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BRC2019 |
Serial |
3275 |
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| Author |
Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam |
| Title |
Subgraph spotting in graph representations of comic book images |
Type |
Journal Article |
| Year |
2018 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
112 |
Issue |
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Pages |
118-124 |
| Keywords |
Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images |
| Abstract |
Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset. |
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DAG; 600.097; 600.121 |
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no |
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Admin @ si @ LLD2018 |
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3150 |
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Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
| Title |
Fast: Facilitated and accurate scene text proposals through fcn guided pruning |
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Journal Article |
| Year |
2019 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
119 |
Issue |
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Pages |
112-120 |
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Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition. |
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DAG; 600.084; 600.121; 600.129 |
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no |
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Admin @ si @ BGN2019 |
Serial |
3342 |
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| Author |
Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
| Title |
Multimodal grid features and cell pointers for scene text visual question answering |
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Journal Article |
| Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
150 |
Issue |
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Pages |
242-249 |
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This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link. |
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DAG; 600.084; 600.121 |
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no |
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Admin @ si @ GBT2021 |
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3620 |
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Oriol Ramos Terrades; Ernest Valveny |
| Title |
A new use of the ridgelets transform for describing linear singularities in images |
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Journal Article |
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2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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27 |
Issue |
6 |
Pages |
587–596 |
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DAG |
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no |
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DAG @ dag @ RaV2006a |
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635 |
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| Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
| Title |
Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
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Journal Article |
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2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
5 |
Pages |
579–588 |
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Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG |
Approved |
no |
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DAG @ dag @ FVS2009a |
Serial |
1114 |
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| Author |
Marçal Rusiñol; Agnes Borras; Josep Llados |
| Title |
Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images |
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Journal Article |
| Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
31 |
Issue |
3 |
Pages |
188–201 |
| Keywords |
Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings |
| Abstract |
This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results. |
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Elsevier |
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Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RBL2010 |
Serial |
1177 |
| Permanent link to this record |
