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| Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
| Title | A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance | Type | Conference Article | ||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3074 - 3079 | ||
Keywords  |
word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance | ||||
| Abstract | Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the 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. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy. | ||||
| Address | Stockholm; Sweden; August 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | Medium | ||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ WEG2014a | Serial | 2515 | ||
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| Author | P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes | ||||
| Title | Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité | Type | Conference Article | ||
| Year | 2014 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
| Volume | Issue | Pages | 233-248 | ||
| Keywords |
word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example | ||||
| 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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| Address | Nancy; Francia; March 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIFED | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ WEG2014c | Serial | 2564 | ||
| Permanent link to this record | |||||
| Author | Ekta Vats; Anders Hast; Alicia Fornes | ||||
| Title | Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1294-1299 | ||
| Keywords |
Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching | ||||
| Abstract | Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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| Address | Sydney; Australia; September 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ VHF2019 | Serial | 3356 | ||
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| Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Segmentation-free Word Spotting with Exemplar SVMs | Type | Journal Article | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 47 | Issue | 12 | Pages | 3967–3978 |
| Keywords |
Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression | ||||
| Abstract | In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage. | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.045; 600.056; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ AGF2014b | Serial | 2485 | ||
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| Author | Suman Ghosh; Ernest Valveny | ||||
| Title | A Sliding Window Framework for Word Spotting Based on Word Attributes | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 652-661 | |
| Keywords |
Word spotting; Sliding window; Word attributes | ||||
| Abstract | In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets. | ||||
| Address | Santiago de Compostela; June 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GhV2015b | Serial | 2716 | ||
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| Author | Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados | ||||
| Title | Unsupervised writer adaptation of whole-word HMMs with application to word-spotting | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 8 | Pages | 742–749 |
| Keywords |
Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis | ||||
| Abstract | In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RPS2010 | Serial | 1290 | ||
| Permanent link to this record | |||||
| Author | Jose Antonio Rodriguez; Florent Perronnin | ||||
| Title | Handwritten word-spotting using hidden Markov models and universal vocabularies | Type | Journal Article | ||
| Year | 2009 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 42 | Issue | 9 | Pages | 2103-2116 |
| Keywords |
Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition | ||||
| Abstract | Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ RoP2009 | Serial | 1053 | ||
| Permanent link to this record | |||||
| Author | David Masip; Alexander Todorov; Jordi Vitria | ||||
| Title | The Role of Facial Regions in Evaluating Social Dime | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision – Workshops and Demonstrations | Abbreviated Journal | |
| Volume | 7584 | Issue | II | Pages | 210-219 |
| Keywords |
Workshops and Demonstrations | ||||
| Abstract | Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics. | ||||
| Address | Florence, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Andrea Fusiello, Vittorio Murino, Rita Cucchiara | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-33867-0 | Medium | |
| Area | Expedition | Conference | ECCVW | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MTV2012 | Serial | 2171 | ||
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