| Records |
| 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. |
| Address |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
0031-3203 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Call Number |
Admin @ si @ RoP2009 |
Serial |
1053 |
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| Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
| Title |
Handwritten Word Spotting with Corrected Attributes |
Type |
Conference Article |
| Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1017-1024 |
| Keywords |
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| Abstract |
We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. |
| Address |
Sydney; Australia; December 2013 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCV |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ AGF2013 |
Serial |
2327 |
| Permanent link to this record |
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| Author |
David Fernandez |
| Title |
Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors |
Type |
Report |
| Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
161 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
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| Corporate Author |
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Thesis |
Master's thesis |
| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ Fer2010b |
Serial |
1353 |
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| |
| Author |
David Fernandez; Josep Llados; Alicia Fornes |
| Title |
Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
628-635 |
| Keywords |
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| Abstract |
There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-3-642-21256-7 |
Medium |
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| Area |
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Expedition |
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Conference |
IbPRIA |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FLF2011 |
Serial |
1742 |
| Permanent link to this record |
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| |
| Author |
Pau Riba; Josep Llados; Alicia Fornes |
| Title |
Handwritten Word Spotting by Inexact Matching of Grapheme Graphs |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
781 - 785 |
| Keywords |
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| Abstract |
This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. |
| Address |
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Thesis |
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Place of Publication |
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Editor |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.077; 600.061; 602.006 |
Approved |
no |
| Call Number |
Admin @ si @ RLF2015b |
Serial |
2642 |
| Permanent link to this record |
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| Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
| Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
Type |
Conference Article |
| Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
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| Volume |
10029 |
Issue |
|
Pages |
543-552 |
| Keywords |
Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
| Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
| Address |
Merida; Mexico; December 2016 |
| Corporate Author |
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Thesis |
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| Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-3-319-49054-0 |
Medium |
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| Area |
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Expedition |
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Conference |
S+SSPR |
| Notes |
DAG; 600.097; 602.006 |
Approved |
no |
| Call Number |
Admin @ si @ TSF2016 |
Serial |
2877 |
| Permanent link to this record |
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| Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez; Petia Radeva; Oriol Pujol |
| Title |
Handwritten Symbol Recognition by a Boosted Blurred Shape Model with Error Correction |
Type |
Book Chapter |
| Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:13–21 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
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| Address |
Girona (Spain) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MILAB;DAG;HUPBA |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2007a |
Serial |
775 |
| Permanent link to this record |
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| Author |
Francisco Cruz; Oriol Ramos Terrades |
| Title |
Handwritten Line Detection via an EM Algorithm |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
718-722 |
| Keywords |
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| Abstract |
In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. |
| Address |
Washington; USA; August 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ CrT2013 |
Serial |
2329 |
| Permanent link to this record |
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| |
| Author |
Arnau Baro; Carles Badal; Pau Torras; Alicia Fornes |
| Title |
Handwritten Historical Music Recognition through Sequence-to-Sequence with Attention Mechanism |
Type |
Conference Article |
| Year |
2022 |
Publication |
3rd International Workshop on Reading Music Systems (WoRMS2021) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
55-59 |
| Keywords |
Optical Music Recognition; Digits; Image Classification |
| Abstract |
Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
| Address |
July 23, 2021, Alicante (Spain) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
WoRMS |
| Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
| Call Number |
Admin @ si @ BBT2022 |
Serial |
3734 |
| Permanent link to this record |
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| Author |
Arnau Baro; Alicia Fornes; Carles Badal |
| Title |
Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism |
Type |
Conference Article |
| Year |
2020 |
Publication |
17th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
|
| Abstract |
Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
| Address |
Virtual ICFHR; September 2020 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICFHR |
| Notes |
DAG; 600.140; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ BFB2020 |
Serial |
3448 |
| Permanent link to this record |
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| Author |
V. Chapaprieta; Ernest Valveny |
| Title |
Handwritten Digit Recognition Using Point Distribution Models. |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:49–54. |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ ChV2001 |
Serial |
83 |
| Permanent link to this record |
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| Author |
Volkmar Frinken; Andreas Fischer; Carlos David Martinez Hinarejos |
| Title |
Handwriting Recognition in Historical Documents using Very Large Vocabularies |
Type |
Conference Article |
| Year |
2013 |
Publication |
2nd International Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
67-72 |
| Keywords |
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| Abstract |
Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words. |
| Address |
Washington; USA; August 2013 |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
978-1-4503-2115-0 |
Medium |
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Expedition |
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Conference |
HIP |
| Notes |
DAG; 600.056; 600.045; 600.061; 602.006; 602.101 |
Approved |
no |
| Call Number |
Admin @ si @ FFM2013 |
Serial |
2296 |
| Permanent link to this record |
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| Author |
Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados |
| Title |
Handwriting Recognition by Attribute embedding and Recurrent Neural Networks |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1038-1043 |
| Keywords |
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| Abstract |
Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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Conference |
ICDAR |
| Notes |
DAG; 600.097; 601.225; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ TDF2017 |
Serial |
3055 |
| Permanent link to this record |
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| Author |
Ernest Valveny; Enric Marti |
| Title |
Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework |
Type |
Conference Article |
| Year |
2000 |
Publication |
Proc. 15th Int Pattern Recognition Conf |
Abbreviated Journal |
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| Volume |
2 |
Issue |
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Pages |
239-242 |
| Keywords |
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| Abstract |
Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm. |
| Address |
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Place of Publication |
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Editor |
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Original Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
0-7695-0750-6 |
Medium |
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Expedition |
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Conference |
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| Notes |
DAG;IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ VAM2000 |
Serial |
1656 |
| Permanent link to this record |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
Hand sign language recognition using multi-view hand skeleton |
Type |
Journal Article |
| Year |
2020 |
Publication |
Expert Systems With Applications |
Abbreviated Journal |
ESWA |
| Volume |
150 |
Issue |
|
Pages |
113336 |
| Keywords |
Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition |
| Abstract |
Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ RKE2020a |
Serial |
3411 |
| Permanent link to this record |
