|
Records |
Links |
|
Author |
Marçal Rusiñol; Lluis Gomez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Avances en clasificación de imágenes en los últimos diez años. Perspectivas y limitaciones en el ámbito de archivos fotográficos históricos |
Type |
Journal |
|
Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2018 |
Publication |
Revista anual de la Asociación de Archiveros de Castilla y León |
Abbreviated Journal |
|
|
|
Volume |
21 |
Issue |
|
Pages |
161-174 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RuG2018 |
Serial |
3239 |
|
Permanent link to this record |
|
|
|
|
Author |
Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
Type |
Journal Article |
|
Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2019 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
|
Volume |
86 |
Issue |
|
Pages |
27-36 |
|
|
Keywords |
Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
|
|
Abstract |
Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ TCF2019 |
Serial |
3166 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Feature Extraction by Using Dual-Generalized Discriminative Common Vectors |
Type |
Journal Article |
|
Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2019 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
|
|
Volume |
61 |
Issue |
3 |
Pages |
331-351 |
|
|
Keywords |
Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning |
|
|
Abstract |
In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods. |
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
DAG; ADAS; 600.084; 600.118; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DRR2019 |
Serial |
3172 |
|
Permanent link to this record |
|
|
|
|
Author |
Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora |
Type |
Journal |
|
Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2019 |
Publication |
Journal for Information Technology Studies as a Human Science |
Abbreviated Journal |
HUMAN IT |
|
|
Volume |
14 |
Issue |
2 |
Pages |
95-120 |
|
|
Keywords |
Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts |
|
|
Abstract |
In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples. |
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
DAG; 600.097; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MVH2019 |
Serial |
3234 |
|
Permanent link to this record |
|
|
|
|
Author |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
|
|
Title |
DSD: document sparse-based denoising algorithm |
Type |
Journal Article |
|
Year ![sorted by Year field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2019 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
|
|
Volume |
22 |
Issue |
1 |
Pages |
177–186 |
|
|
Keywords |
Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models |
|
|
Abstract |
In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. |
|
|
Address |
|
|
|
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 |
|
|
|
Notes |
DAG; 600.097; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DRT2019 |
Serial |
3254 |
|
Permanent link to this record |