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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Tony Pridmore; Dimosthenis Karatzas |
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Title |
Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems |
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Journal Article |
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Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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Volume |
13 |
Issue |
3 |
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187-207 |
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This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results. |
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Springer-Verlag |
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1433-2833 |
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DAG @ dag @ DVP2010 |
Serial |
1289 |
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Author |
Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados |
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Title |
Symbol Recognition Using a Concept Lattice of Graphical Patterns |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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Volume |
6020 |
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187-198 |
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In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest. |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN |
978-3-642-13727-3 |
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DAG |
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Call Number |
Admin @ si @ RBO2010 |
Serial |
2407 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
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Title |
Symbol-independent writer identification in old handwritten music scores |
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Conference Article |
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Year |
2009 |
Publication |
In proceedings of 8th IAPR International Workshop on Graphics Recognition |
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186–197 |
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La Rochelle, France |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG @ dag @ FLS2009a |
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1222 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar |
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Title |
Self-Supervised Visual Representations for Cross-Modal Retrieval |
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Conference Article |
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Year |
2019 |
Publication |
ACM International Conference on Multimedia Retrieval |
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Pages |
182–186 |
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Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. |
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Otawa; Canada; june 2019 |
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ICMR |
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DAG; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ PGR2019 |
Serial |
3288 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas |
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Title |
Text Segmentation in Colour Posters from the Spanish Civil War Era |
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Conference Article |
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Year |
2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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Pages |
181 - 185 |
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Abstract |
The extraction of textual content from colour documents of a graphical nature is a complicated task. The text can be rendered in any colour, size and orientation while the existence of complex background graphics with repetitive patterns can make its localization and segmentation extremely difficult.
Here, we propose a new method for extracting textual content from such colour images that makes no assumption as to the size of the characters, their orientation or colour, while it is tolerant to characters that do not follow a straight baseline. We evaluate this method on a collection of documents with historical
connotations: the Posters from the Spanish Civil War. |
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Address |
Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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Call Number |
DAG @ dag @ ClK2009 |
Serial |
1172 |
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Author |
H. Chouaib; Salvatore Tabbone; Oriol Ramos Terrades; F. Cloppet; N. Vincent; A.T. Thierry Paquet |
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Title |
Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost |
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Conference Article |
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Year |
2008 |
Publication |
Colloque International Francophone sur l'Ecrit et le Document |
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181-186 |
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Rouen, France |
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CIFED |
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DAG |
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no |
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Call Number |
Admin @ si @ CTR2008 |
Serial |
1874 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier |
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Title |
Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images |
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Conference Article |
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Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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181 - 185 |
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Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 601.223; 600.077 |
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Admin @ si @ RCO2014a |
Serial |
2545 |
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Author |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone |
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Title |
DSD: document sparse-based denoising algorithm |
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Journal Article |
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Year |
2019 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
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22 |
Issue |
1 |
Pages |
177–186 |
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Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models |
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In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. |
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DAG; 600.097; 600.140; 600.121 |
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Admin @ si @ DRT2019 |
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3254 |
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Author |
Joan Mas; J.A. Jorge; Gemma Sanchez; Josep Llados |
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Title |
Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities, |
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5046 |
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176–187 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG @ dag @ MJS2008 |
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991 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network |
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Conference Article |
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2022 |
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Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
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13639 |
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171-184 |
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Object detection; Optical music recognition; Graph neural network |
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Abstract |
During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. |
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December 04 – 07, 2022; Hyderabad, India |
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ICFHR |
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DAG; 600.162; 600.140; 602.230 |
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Admin @ si @ BRF2022b |
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3740 |
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