| |
Records |
Links |
|
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; Anders Hast; Alicia Fornes |
|
| |
Title |
Word Spotting as a Tool for Scribal Attribution |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
2nd Conference of the association of Digital Humanities in the Nordic Countries |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
87-89 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Gothenburg; Suecia; March 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 |
978-91-88348-83-8 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
DHN |
|
| |
Notes |
DAG; 600.097; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MHF2017 |
Serial |
2954 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo |
|
| |
Title |
Reading Text in the Wild from Compressed Images |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
1st International workshop on Egocentric Perception, Interaction and Computing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant
impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates. |
|
| |
Address |
Venice; Italy; October 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 |
ICCV - EPIC |
|
| |
Notes |
DAG; 600.084; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GBS2017 |
Serial |
3006 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
70 |
Issue |
|
Pages |
60-74 |
|
| |
Keywords |
|
|
| |
Abstract |
Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
|
| |
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; 600.084; 601.197; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GoK2017 |
Serial |
2886 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
|
| |
Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
67 |
Issue |
|
Pages |
85-96 |
|
| |
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; 600.084; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GNK2017 |
Serial |
2887 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
|
| |
Title |
LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset. |
|
| |
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; 600.084; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GRK2017 |
Serial |
2999 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
|
| |
Title |
Self‐supervised learning of visual features through embedding images into text topic spaces |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
30th IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches. |
|
| |
Address |
Honolulu; Hawaii; July 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 |
CVPR |
|
| |
Notes |
DAG; 600.084; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GPR2017 |
Serial |
2889 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
|
| |
Title |
Ontology-Based Understanding of Architectural Drawings |
Type |
Book Chapter |
| |
Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
Abbreviated Journal |
|
|
| |
Volume |
9657 |
Issue |
|
Pages |
75-85 |
|
| |
Keywords |
Graphics recognition; Floor plan analysi; Domain ontology |
|
| |
Abstract |
In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HRL2017 |
Serial |
3086 |
|
| 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 |
Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
ICDAR2017 Robust Reading Challenge on Omnidirectional Video |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community. |
|
| |
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; 600.084; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ IMT2017 |
Serial |
3077 |
|
| Permanent link to this record |
