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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Novel Learning-free Word Spotting Approach Based on Graph Representation |
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Conference Article |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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207-211 |
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Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of 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. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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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; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014b |
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2517 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité |
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Conference Article |
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2014 |
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Colloque International Francophone sur l'Écrit et le Document |
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233-248 |
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word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example |
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Abstract |
Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Nancy; Francia; March 2014 |
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CIFED |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014c |
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2564 |
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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 |
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Conference Article |
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2015 |
Publication |
10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition |
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9069 |
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208-217 |
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Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding |
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Abstract |
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 in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph 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. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. 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 a handwritten word spotting scenario in images of historical documents. |
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Beijing; China; May 2015 |
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Springer International Publishing |
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C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng |
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0302-9743 |
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978-3-319-18223-0 |
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GbRPR |
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Notes |
DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ RLF2015a |
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2618 |
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Author |
Nuria Cirera; Alicia Fornes; Josep Llados |
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Title |
Hidden Markov model topology optimization for handwriting recognition |
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Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
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626-630 |
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In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Nancy; France; August 2015 |
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Notes |
DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ CFL2015 |
Serial |
2639 |
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Author |
Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
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Title |
Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
Type |
Conference Article |
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Year |
2015 |
Publication |
E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
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139-141 |
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Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
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In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
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Bern; Switzerland; September 2015 |
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VoteID |
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DAG; 600.061; 602.006; 600.077 |
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Call Number |
Admin @ si @ TCP2015 |
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2641 |
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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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Conference Article |
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Year |
2017 |
Publication |
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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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 |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Banknote counterfeit detection through background texture printing analysis |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
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Rumania; May 2016 |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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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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DAG; 600.061; 600.097; 600.121 |
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Admin @ si @ BRL2018 |
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3084 |
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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
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Conference Article |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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1-6 |
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Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAG; 600.061; 600.084; 600.077 |
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Admin @ si @ BRC2016 |
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2757 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Sparse representation over learned dictionary for symbol recognition |
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2016 |
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Signal Processing |
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SP |
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125 |
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36-47 |
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Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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