|
Records |
Links |
|
Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes |


|
|
Title |
Graph-based deep learning for graphics classification |
Type |
Conference Article |
|
Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
29-30 |
|
|
Keywords |
|
|
|
Abstract |
Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems |
|
|
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 |
ICDAR |
|
|
Notes  |
DAG; 600.097; 601.302; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RDL2017b |
Serial |
3058 |
|
Permanent link to this record |
|
|
|
|
Author |
Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados |


|
|
Title |
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records |
Type |
Conference Article |
|
Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1389-1394 |
|
|
Keywords |
|
|
|
Abstract |
The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. |
|
|
Address |
Kyoto; Japan; November 2017 |
|
|
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; 600.097; 601.225; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ FRB2017 |
Serial |
3052 |
|
Permanent link to this record |
|
|
|
|
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 |
|
|
|
Volume |
|
Issue |
|
Pages |
1038-1043 |
|
|
Keywords |
|
|
|
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 |
|
|
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 |
ICDAR |
|
|
Notes  |
DAG; 600.097; 601.225; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ TDF2017 |
Serial |
3055 |
|
Permanent link to this record |
|
|
|
|
Author |
Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes |

|
|
Title |
In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora |
Type |
Journal |
|
Year |
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 |

|
|
Title |
DSD: document sparse-based denoising algorithm |
Type |
Journal Article |
|
Year |
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 |
|
|
|
|
Author |
Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi |


|
|
Title |
Towards a generic unsupervised method for transcription of encoded manuscripts |
Type |
Conference Article |
|
Year |
2019 |
Publication |
3rd International Conference on Digital Access to Textual Cultural Heritage |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
73-78 |
|
|
Keywords |
A. Baró, J. Chen, A. Fornés, B. Megyesi. |
|
|
Abstract |
Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods. |
|
|
Address |
Brussels; May 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 |
DATeCH |
|
|
Notes  |
DAG; 600.097; 600.140; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BCF2019 |
Serial |
3276 |
|
Permanent link to this record |
|
|
|
|
Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados |

|
|
Title |
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework |
Type |
Conference Article |
|
Year |
2018 |
Publication |
14th Asian Conference on Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset. |
|
|
Address |
Perth; Australia; December 2018 |
|
|
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 |
ACCV |
|
|
Notes  |
DAG; 600.097; 600.121; 600.129 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DDG2018a |
Serial |
3151 |
|
Permanent link to this record |
|
|
|
|
Author |
Marçal Rusiñol; Josep Llados |

|
|
Title |
Flowchart Recognition in Patent Information Retrieval |
Type |
Book Chapter |
|
Year |
2017 |
Publication |
Current Challenges in Patent Information Retrieval |
Abbreviated Journal |
|
|
|
Volume |
37 |
Issue |
|
Pages |
351-368 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
M. Lupu; K. Mayer; N. Kando; A.J. Trippe |
|
|
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; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RuL2017 |
Serial |
2896 |
|
Permanent link to this record |
|
|
|
|
Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |

|
|
Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
Type |
Book Chapter |
|
Year |
2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
|
|
|
Volume |
9657 |
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
B. Lamiroy; R Dueire Lins |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
GREC |
|
|
Notes  |
DAG; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ JLR2017 |
Serial |
2928 |
|
Permanent link to this record |
|
|
|
|
Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |


|
|
Title |
Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs |
Type |
Conference Article |
|
Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines |
|
|
Abstract |
We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction. |
|
|
Address |
Faro; Portugal; June 2017 |
|
|
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 |
IbPRIA |
|
|
Notes  |
DAG; 600.097; 600.121 |
Approved |
no |
|
|
Call Number |
Admin @ si @ JRL2017a |
Serial |
2953 |
|
Permanent link to this record |