| |
Records |
Links |
|
Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
|
| |
Title |
Word Spotting in Scene Images based on Character Recognition |
Type  |
Conference Article |
| |
Year |
2018 |
Publication |
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1872-1874 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images. |
|
| |
Address |
Salt Lake City; USA; June 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 |
CVPRW |
|
| |
Notes |
DAG; 600.129; 600.121 |
Approved |
no |
|
| |
Call Number |
BKB2018a |
Serial |
3179 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
|
| |
Title |
Automatic Verification of Properly Signed Multi-page Document Images |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
Proceedings of the Eleventh International Symposium on Visual Computing |
Abbreviated Journal |
|
|
| |
Volume |
9475 |
Issue |
|
Pages |
327-336 |
|
| |
Keywords |
Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
|
| |
Abstract |
In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
|
| |
Address |
Las Vegas, Nevada, USA; December 2015 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
9475 |
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ISVC |
|
| |
Notes |
DAG; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3189 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Giacomo Magnifico; Beata Megyesi; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
|
| |
Title |
Lost in Transcription of Graphic Signs in Ciphers |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
International Conference on Historical Cryptology (HistoCrypt 2022) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
153-158 |
|
| |
Keywords |
transcription of ciphers; hand-written text recognition of symbols; graphic signs |
|
| |
Abstract |
Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings. |
|
| |
Address |
Amsterdam, Netherlands, June 20-22, 2022 |
|
| |
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 |
HystoCrypt |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MBS2022 |
Serial |
3731 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
|
| |
Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
4th Women in Computer Vision Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1941-19412 |
|
| |
Keywords |
Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
|
| |
Abstract |
We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
|
| |
Address |
Salt Lake City; USA; June 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 |
WiCV |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DBR2018 |
Serial |
3222 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Arnau Baro; Pau Riba; Alicia Fornes |
|
| |
Title |
A Starting Point for Handwritten Music Recognition |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
1st International Workshop on Reading Music Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
5-6 |
|
| |
Keywords |
Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
|
| |
Abstract |
In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
|
| |
Address |
Paris; France; September 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 |
WORMS |
|
| |
Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRF2018 |
Serial |
3223 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Ali Furkan Biten; Lluis Gomez; Jaume Gibert; Marçal Rusiñol; Dimosthenis Karatzas |
|
| |
Title |
Selective Style Transfer for Text |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
15th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
805-812 |
|
| |
Keywords |
transfer; text style transfer; data augmentation; scene text detection |
|
| |
Abstract |
This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means
transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available. |
|
| |
Address |
Sydney; Australia; September 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 |
ICDAR |
|
| |
Notes |
DAG; 600.129; 600.135; 601.338; 601.310; 600.121 |
Approved |
no |
|
| |
Call Number |
GBG2019 |
Serial |
3265 |
|
| 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 |
Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas |
|
| |
Title |
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step. |
|
| |
Address |
Aspen; Colorado; USA; March 2020 |
|
| |
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 |
WACV |
|
| |
Notes |
DAG; 600.129; 600.140; 601.302; 601.312; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRF2020 |
Serial |
3446 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
Exploring Hate Speech Detection in Multimodal Publications |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
In this work we target the problem of hate speech detection in multimodal publications formed by a text and an image. We gather and annotate a large scale dataset from Twitter, MMHS150K, and propose different models that jointly analyze textual and visual information for hate speech detection, comparing them with unimodal detection. We provide quantitative and qualitative results and analyze the challenges of the proposed task. We find that, even though images are useful for the hate speech detection task, current multimodal models cannot outperform models analyzing only text. We discuss why and open the field and the dataset for further research. |
|
| |
Address |
Aspen; March 2020 |
|
| |
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 |
WACV |
|
| |
Notes |
DAG; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2020a |
Serial |
3280 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; Lluis Gomez; A. Landman; M. Silva Constenla; Dimosthenis Karatzas |
|
| |
Title |
Automatic Structured Text Reading for License Plates and Utility Meters |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial
numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients. |
|
| |
Address |
Cardiff; UK; September 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 |
BMVC-VAIE19 |
|
| |
Notes |
DAG; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGL2019 |
Serial |
3283 |
|
| Permanent link to this record |
