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N.Nayef; F.Yin; I.Bizid; H.Choi; Y.Feng; Dimosthenis Karatzas; Z.Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W.Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L.Liu; Jean-Marc Ogier |
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Title |
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT |
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Conference Article |
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2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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1454-1459 |
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Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge. |
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Kyoto; Japan; November 2017 |
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978-1-5386-3586-5 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ NYB2017 |
Serial |
3097 |
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Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes |
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Title |
La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades |
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Conference Article |
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2017 |
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3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional |
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281-383 |
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978-84-697-5692-8 |
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HDH |
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DAG; 600.121 |
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Admin @ si @ VFV2017 |
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3060 |
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Author |
Pau Riba; Alicia Fornes; Josep Llados |
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Title |
Towards the Alignment of Handwritten Music Scores |
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Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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103-116 |
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Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment |
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It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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Bart Lamiroy; R Dueire Lins |
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LNCS |
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978-3-319-52158-9 |
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DAG; 600.097; 602.006; 600.121 |
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Admin @ si @ RFL2017 |
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2955 |
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Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes |
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Title |
Graph-based deep learning for graphics classification |
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Conference Article |
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Year |
2017 |
Publication |
12th IAPR International Workshop on Graphics Recognition |
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29-30 |
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Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems |
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GREC |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RDL2017b |
Serial |
3058 |
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Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes; Sounak Dey |
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Title |
Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms |
Type |
Conference Article |
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Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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Pages |
475-480 |
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Keywords |
document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion |
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Abstract |
Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the
state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario |
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ICDAR |
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DAG; 600.097; 601.302; 600.121 |
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Call Number |
Admin @ si @ RDL2017a |
Serial |
3053 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
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Conference Article |
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Year |
2017 |
Publication |
11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
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10310 |
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107-117 |
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Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
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Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
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Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RLF2017a |
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2951 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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2017 |
Publication |
Pattern Recognition Letters |
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PRL |
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87 |
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203-211 |
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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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DAG; 600.097; 602.006; 603.053; 600.121 |
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RLF2017b |
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2873 |
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Author |
Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas |
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Title |
ICDAR2017 Robust Reading Challenge on COCO-Text |
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Conference Article |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ GSG2017 |
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3076 |
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Author |
Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal |
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Title |
Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario |
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Conference Article |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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31-32 |
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One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDL2017 |
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3057 |
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Sounak Dey; Palaiahnakote Shivakumara; K.S. Raghunanda; Umapada Pal; Tong Lu; G. Hemantha Kumar; Chee Seng Chan |
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Title |
Script independent approach for multi-oriented text detection in scene image |
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2017 |
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Neurocomputing |
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NEUCOM |
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242 |
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96-112 |
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Developing a text detection method which is invariant to scripts in natural scene images is a challeng- ing task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on convex hull to generate radius matrices using RRT. It is a fact that RRT pro- vides low radius values for the pixels that are near to edges, constant radius values for the pixels that represent stroke width, and high radius values that represent holes created in background and convex hull because of the regular structures of text components. We apply k -means clustering on the radius matrices to group such spatially coherent regions into individual clusters. Then the proposed method studies the radius values of such cluster components that are close to the centroid and far from the cen- troid to detect text components. Furthermore, we have developed a Bangla dataset (named as ISI-UM dataset) and propose a semi-automatic system for generating its ground truth for text detection of arbi- trary orientations, which can be used by the researchers for text detection and recognition in the future. The ground truth will be released to public. Experimental results on our ISI-UM data and other standard datasets, namely, ICDAR 2013 scene, SVT and MSRA data, show that the proposed method outperforms the existing methods in terms of multi-lingual and multi-oriented text detection ability. |
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DAG; 600.121 |
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Admin @ si @ DSR2017 |
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3260 |
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