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Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
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Journal Article |
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Year |
2017 |
Publication |
Pattern Recognition |
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PR |
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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 |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-script Text Extraction from Natural Scenes |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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467-471 |
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Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.056; 601.158; 601.197 |
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Call Number |
Admin @ si @ GoK2013 |
Serial |
2310 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
MSER-based Real-Time Text Detection and Tracking |
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Conference Article |
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Year |
2014 |
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22nd International Conference on Pattern Recognition |
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3110 - 3115 |
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We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices. |
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Stockholm; August 2014 |
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1051-4651 |
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ICPR |
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DAG; 600.056; 601.158; 601.197; 600.077 |
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Admin @ si @ GoK2014a |
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2492 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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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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IWRR |
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DAG; 600.077 |
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no |
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Admin @ si @ GoK2014c |
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2538 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
Object Proposals for Text Extraction in the Wild |
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Conference Article |
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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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ICDAR |
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DAG; 600.077; 600.084; 601.197 |
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Admin @ si @ GoK2015 |
Serial |
2691 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
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Journal Article |
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Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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19 |
Issue |
4 |
Pages |
335-349 |
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Keywords |
scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
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Abstract |
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 |
Serial |
2862 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fine-grained approach to scene text script identification |
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Conference Article |
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Year |
2016 |
Publication |
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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Notes |
DAG; 601.197; 600.084 |
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no |
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Call Number |
Admin @ si @ GoK2016b |
Serial |
2863 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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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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Year |
2017 |
Publication |
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 |
Raul Gomez |
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Title |
Exploiting the Interplay between Visual and Textual Data for Scene Interpretation |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data.
We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert |
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978-84-121011-7-1 |
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DAG; 600.121 |
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Admin @ si @ Gom20 |
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3479 |
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Author |
Lluis Gomez |
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Title |
Perceptual Organization for Text Extraction in Natural Scenes |
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2012 |
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CVC Technical Report |
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173 |
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Bellaterra |
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Master's thesis |
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DAG |
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Admin @ si @ Gom2012 |
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