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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Evaluation of Texture Descriptors for Validation of Counterfeit Documents |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1237-1242 |
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This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance. |
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2379-2140 |
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ICDAR |
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DAG; 600.061; 601.269; 600.097; 600.121 |
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Admin @ si @ BRL2017 |
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3092 |
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Author |
Alicia Fornes; Beata Megyesi; Joan Mas |
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Title |
Transcription of Encoded Manuscripts with Image Processing Techniques |
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Conference Article |
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2017 |
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Digital Humanities Conference |
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441-443 |
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DH |
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DAG; 600.097; 600.121 |
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Admin @ si @ FMM2017 |
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3061 |
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Author |
Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados |
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Title |
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records |
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Conference Article |
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Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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1389-1394 |
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The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.097; 601.225; 600.121 |
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Admin @ si @ FRB2017 |
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3052 |
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Author |
Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification |
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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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33-38 |
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graph embedding; hierarchical graph representation; graph clustering; stochastic graphlet embedding; graph classification |
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Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).
Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support
vector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols |
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ICDAR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ DRL2017 |
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3054 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Recurrent Neural Networks |
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Conference Article |
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2017 |
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14th IAPR International Workshop on Graphics Recognition |
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25-26 |
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Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
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Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
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ICDAR |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ BRC2017 |
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3056 |
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Author |
ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao |
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Title |
ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) |
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Conference Article |
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Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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1444-1447 |
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Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. |
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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 @ YCY2017 |
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3098 |
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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo |
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Title |
Automatic Static/Variable Content Separation in Administrative Document Images |
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Conference Article |
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Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.084; 600.121 |
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Admin @ si @ ART2017 |
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3001 |
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Author |
Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol |
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Title |
The Robust Reading Competition Annotation and Evaluation Platform |
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Conference Article |
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2017 |
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1st International Workshop on Open Services and Tools for Document Analysis |
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The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation
of data, and to provide online and offline performance evaluation and analysis services |
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Kyoto; Japan; November 2017 |
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ICDAR-OST |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ KGR2017 |
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3063 |
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Author |
E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara |
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Title |
Benchmarking Keypoint Filtering Approaches for Document Image Matching |
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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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Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.084; 600.121 |
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Admin @ si @ RCR2017 |
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3000 |
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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
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Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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LNCS |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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