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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Scene Text Recognition: No Country for Old Men? |
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Conference Article |
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2014 |
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1st International Workshop on Robust Reading |
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DAG; 600.077 |
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Admin @ si @ GoK2014c |
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2538 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Object Proposals for Text Extraction in the Wild |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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206 - 210 |
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Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available |
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DAG; 600.077; 600.084; 601.197 |
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2691 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
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2016 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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19 |
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4 |
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335-349 |
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scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
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Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, 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 hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
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DAG; 600.056; 601.197 |
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Admin @ si @ GoK2016a |
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2862 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fine-grained approach to scene text script identification |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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192-197 |
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This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online. |
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Santorini; Grecia; April 2016 |
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DAS |
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DAG; 601.197; 600.084 |
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Admin @ si @ GoK2016b |
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2863 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
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Journal Article |
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2017 |
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Pattern Recognition |
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PR |
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70 |
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60-74 |
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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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DAG; 600.084; 601.197; 600.121; 600.129 |
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Admin @ si @ GoK2017 |
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2886 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
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Title |
Historical review of scene text detection research |
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Book Chapter |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Admin @ si @ GBK2020 |
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3495 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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GNR2020 |
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3494 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Improving patch‐based scene text script identification with ensembles of conjoined networks |
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2017 |
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Pattern Recognition |
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67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GNK2017 |
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2887 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Single Shot Scene Text Retrieval |
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Conference Article |
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2018 |
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15th European Conference on Computer Vision |
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11218 |
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728-744 |
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Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
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Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed. |
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Munich; September 2018 |
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ECCV |
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DAG; 600.084; 601.338; 600.121; 600.129 |
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Admin @ si @ GMR2018 |
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3143 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Multimodal grid features and cell pointers for scene text visual question answering |
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2021 |
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Pattern Recognition Letters |
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PRL |
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150 |
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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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Admin @ si @ GBT2021 |
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3620 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez |
![download PDF file pdf](img/file_PDF.gif)
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Perceptual Organization for Text Extraction in Natural Scenes |
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2012 |
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CVC Technical Report |
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Bellaterra |
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Master's thesis |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Gomez |
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Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding |
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2016 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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. |
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Ph.D. thesis |
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Dimosthenis Karatzas |
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Admin @ si @ Gom2016 |
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2891 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Garrido; M.Guerrieri; Laura Igual |
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Image Segmentation with Cage Active Contours |
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Journal Article |
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Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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24 |
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12 |
Pages |
5557 - 5566 |
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Keywords |
Level sets; Mean value coordinates; Parametrized active contours; level sets; mean value coordinates |
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Abstract |
In this paper, we present a framework for image segmentation based on parametrized active contours. The evolving contour is parametrized according to a reduced set of control points that form a closed polygon and have a clear visual interpretation. The parametrization, called mean value coordinates, stems from the techniques used in computer graphics to animate virtual models. Our framework allows to easily formulate region-based energies to segment an image. In particular, we present three different local region-based energy terms: 1) the mean model; 2) the Gaussian model; 3) and the histogram model. We show the behavior of our method on synthetic and real images and compare the performance with state-of-the-art level set methods. |
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1057-7149 |
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MILAB |
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no |
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Admin @ si @ GGI2015 |
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2673 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Barcelo; X. Binefa |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Bayesian Video Mosaicing with Moving Objects. |
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Miscellaneous |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:91–96. |
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Admin @ si @ BaB2001 |
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72 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Lluis Barcelo; X. Binefa |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Bayesian Video Mosaicing with moving objects |
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Journal |
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2002 |
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International Journal of Pattern Recognition and Artificial Intelligence, 16(3): 341–348 (IF: 0.359) |
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no |
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Admin @ si @ BaB2002 |
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268 |
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