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Author |
Klara Janousckova; Jiri Matas; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Text Recognition – Real World Data and Where to Find Them |
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Conference Article |
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2020 |
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25th International Conference on Pattern Recognition |
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4489-4496 |
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We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets 1 1 Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya. |
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Virtual; January 2021 |
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DAG; 600.121; 600.129 |
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Admin @ si @ JMG2020 |
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3557 |
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Ali Furkan Biten; Ruben Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
Scene Text Visual Question Answering |
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Conference Article |
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2019 |
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18th IEEE International Conference on Computer Vision |
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4291-4301 |
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Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research. |
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Seul; Corea; October 2019 |
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ICCV |
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DAG; 600.129; 600.135; 601.338; 600.121 |
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Admin @ si @ BTM2019b |
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3285 |
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Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge |
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A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams |
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2010 |
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Pattern Recognition |
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PR |
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43 |
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12 |
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4148–4164 |
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Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing |
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This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. |
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Elsevier |
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DAG |
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DAG @ dag @ MLS2010 |
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1336 |
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Author |
Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados |
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Title |
Multi-oriented Bangla and Devnagari text recognition |
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Journal Article |
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2010 |
Publication |
Pattern Recognition |
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PR |
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43 |
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12 |
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4124–4136 |
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There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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Elsevier |
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DAG |
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DAG @ dag @ PRT2010 |
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1337 |
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Author |
Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-modal reasoning graph for scene-text based fine-grained image classification and retrieval |
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Conference Article |
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2021 |
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IEEE Winter Conference on Applications of Computer Vision |
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4022-4032 |
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Virtual; January 2021 |
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WACV |
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DAG; 600.121 |
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Admin @ si @ MDB2021 |
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3491 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Segmentation-free Word Spotting with Exemplar SVMs |
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Journal Article |
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2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
Issue |
12 |
Pages |
3967–3978 |
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Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression |
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In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. |
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DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ AGF2014b |
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2485 |
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Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
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Title |
A Content Spotting System For Line Drawing Graphic Document Images |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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20 |
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3420–3423 |
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We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents. |
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1051-4651 |
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978-1-4244-7542-1 |
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DAG @ dag @ LBR2010b |
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1460 |
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Lluis Gomez; Dimosthenis Karatzas |
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MSER-based Real-Time Text Detection and Tracking |
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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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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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Jon Almazan; Alicia Fornes; Ernest Valveny |
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A non-rigid appearance model for shape description and recognition |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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9 |
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3105--3113 |
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Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition |
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In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. |
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0031-3203 |
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DAG @ dag @ AFV2012 |
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1982 |
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P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance |
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2014 |
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22nd International Conference on Pattern Recognition |
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3074 - 3079 |
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word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014a |
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2515 |
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