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Author |
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Title |
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
31 |
Issue |
8 |
Pages |
742–749 |
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Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
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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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Elsevier |
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DAG |
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DAG @ dag @ RPS2010 |
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1290 |
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Author |
Suman Ghosh; Ernest Valveny |
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Title |
A Sliding Window Framework for Word Spotting Based on Word Attributes |
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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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Volume |
9117 |
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Pages |
652-661 |
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Keywords |
Word spotting; Sliding window; Word attributes |
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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. |
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Santiago de Compostela; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19389-2 |
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IbPRIA |
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DAG; 600.077 |
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no |
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Admin @ si @ GhV2015b |
Serial |
2716 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Segmentation-free Word Spotting with Exemplar SVMs |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
12 |
Pages |
3967–3978 |
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Keywords |
Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression |
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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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DAG; 600.045; 600.056; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ AGF2014b |
Serial |
2485 |
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Author |
Ekta Vats; Anders Hast; Alicia Fornes |
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Title |
Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion |
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Conference Article |
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Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
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1294-1299 |
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Keywords |
Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching |
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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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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.140; 600.121 |
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no |
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Call Number |
Admin @ si @ VHF2019 |
Serial |
3356 |
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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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Year |
2014 |
Publication |
Colloque International Francophone sur l'Écrit et le Document |
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233-248 |
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Keywords |
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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Notes |
DAG; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ WEG2014c |
Serial |
2564 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3074 - 3079 |
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Keywords |
word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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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. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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ICPR |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ WEG2014a |
Serial |
2515 |
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Author |
David Aldavert; Marçal Rusiñol |
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Title |
Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting |
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Conference Article |
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2018 |
Publication |
13th IAPR International Workshop on Document Analysis Systems |
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223 - 228 |
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Keywords |
Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information |
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Abstract |
Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 600.121 |
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Admin @ si @ AlR2018b |
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3105 |
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Author |
Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
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Title |
MUST-VQA: MUltilingual Scene-text VQA |
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Conference Article |
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2022 |
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Proceedings European Conference on Computer Vision Workshops |
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13804 |
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345–358 |
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Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
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In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. |
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Tel-Aviv; Israel; October 2022 |
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ECCVW |
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DAG; 302.105; 600.155; 611.002 |
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Admin @ si @ VBM2022 |
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3770 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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7 |
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1898-1905 |
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visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ GPV2013 |
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2306 |
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Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Modelling task-dependent eye guidance to objects in pictures |
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2014 |
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Cognitive Computation |
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CoCom |
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6 |
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3 |
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558-584 |
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Visual attention; Gaze guidance; Value; Payoff; Stochastic fixation prediction |
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5Y Impact Factor: 1.14 / 3rd (Computer Science, Artificial Intelligence)
We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general action-perception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the action-perception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data derived from publicly available datasets and from our own experiments. |
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Springer US |
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1866-9956 |
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DAG; 600.056; 600.045; 605.203; 601.212; 600.077 |
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Admin @ si @ CKL2014 |
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2419 |
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