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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Dynamic Lexicon Generation for Natural Scene Images |
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Conference Article |
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Year |
2016 |
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14th European Conference on Computer Vision Workshops |
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395-410 |
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scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN |
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Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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DAG; 600.084 |
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Admin @ si @ PGR2016 |
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2825 |
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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
e-Counterfeit: a mobile-server platform for document counterfeit detection |
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Conference Article |
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2017 |
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14th IAPR International Conference on Document Analysis and Recognition |
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This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms. |
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Kyoto; Japan; November 2017 |
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ICDAR |
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DAG; 600.061; 600.097; 600.121 |
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Admin @ si @ BRL2018 |
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3084 |
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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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Year |
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 |
Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
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Title |
A Transcription Is All You Need: Learning to Align through Attention |
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Conference Article |
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Year |
2021 |
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14th IAPR International Workshop on Graphics Recognition |
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12916 |
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141–146 |
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Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. |
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Virtual; September 2021 |
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LNCS |
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GREC |
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DAG; 602.230; 600.140; 600.121 |
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Admin @ si @ TSC2021 |
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3619 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting |
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Conference Article |
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2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington 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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Call Number |
Admin @ si @ GRK2017 |
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2999 |
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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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Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
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Abstract |
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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Call Number |
Admin @ si @ RCR2017 |
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3000 |
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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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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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Call Number |
Admin @ si @ ART2017 |
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3001 |
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Author |
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 |
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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 |
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3097 |
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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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2017 |
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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 |
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 |
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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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475-480 |
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document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion |
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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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Admin @ si @ RDL2017a |
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3053 |
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