| |
Records |
Links |
|
Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
|
| |
Title |
16th International Conference, 2021, Proceedings, Part II |
Type |
Book Whole |
| |
Year |
2021 |
Publication  |
Document Analysis and Recognition – ICDAR 2021 |
Abbreviated Journal |
|
|
| |
Volume |
12822 |
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
|
| |
Address |
Lausanne, Switzerland, September 5-10, 2021 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer Cham |
Place of Publication |
|
Editor |
Josep Llados; Daniel Lopresti; Seiichi Uchida |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-3-030-86330-2 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICDAR |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3726 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mickael Coustaty; Alicia Fornes |
|
| |
Title |
Document Analysis and Recognition – ICDAR 2023 Workshops |
Type |
Book Whole |
| |
Year |
2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
Abbreviated Journal |
|
|
| |
Volume |
14194 |
Issue |
2 |
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
San Jose; USA; August 2023 |
|
| |
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 |
ICDAR |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CoF2023 |
Serial |
3852 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ernest Valveny; Philippe Dosch |
|
| |
Title |
Performance Evaluation of Symbol Recognition |
Type |
Book Chapter |
| |
Year |
2004 |
Publication |
Document Analysis Systems |
Abbreviated Journal |
LNCS |
|
| |
Volume |
3163 |
Issue |
|
Pages |
354–365 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Springer-Verlag |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
S. Marinai, A. Dengel (Eds.), |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
3-540-23060-2 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
DAG @ dag @ VaD2004a |
Serial |
502 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Gemma Sanchez; Ernest Valveny; Josep Llados; Joan Mas; N. Lozano |
|
| |
Title |
A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario |
Type |
Miscellaneous |
| |
Year |
2004 |
Publication |
Document Analysis Systems VI, S. Marinai, A. Dengel (Eds.) Lecture Notes in Computer Science, 3163:389–400 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Springer-Verlag |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
DAG @ dag @ SVL2004 |
Serial |
460 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
|
| |
Title |
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Document Analysis Systems.15th IAPR International Workshop, (DAS2022) |
Abbreviated Journal |
|
|
| |
Volume |
13237 |
Issue |
|
Pages |
583–597 |
|
| |
Keywords |
Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG |
|
| |
Abstract |
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. |
|
| |
Address |
La Rochelle, France; May 22–25, 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 |
DAS |
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MGR2022 |
Serial |
3694 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Josep Brugues Pujolras; Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
A Multilingual Approach to Scene Text Visual Question Answering |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Document Analysis Systems.15th IAPR International Workshop, (DAS2022) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
65-79 |
|
| |
Keywords |
Scene text; Visual question answering; Multilingual word embeddings; Vision and language; Deep learning |
|
| |
Abstract |
Scene Text Visual Question Answering (ST-VQA) has recently emerged as a hot research topic in Computer Vision. Current ST-VQA models have a big potential for many types of applications but lack the ability to perform well on more than one language at a time due to the lack of multilingual data, as well as the use of monolingual word embeddings for training. In this work, we explore the possibility to obtain bilingual and multilingual VQA models. In that regard, we use an already established VQA model that uses monolingual word embeddings as part of its pipeline and substitute them by FastText and BPEmb multilingual word embeddings that have been aligned to English. Our experiments demonstrate that it is possible to obtain bilingual and multilingual VQA models with a minimal loss in performance in languages not used during training, as well as a multilingual model trained in multiple languages that match the performance of the respective monolingual baselines. |
|
| |
Address |
La Rochelle, France; May 22–25, 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 |
DAS |
|
| |
Notes |
DAG; 611.004; 600.155; 601.002 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BGK2022b |
Serial |
3695 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
|
| |
Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases |
Type |
Conference Article |
| |
Year |
2014 |
Publication |
Document Recognition and Retrieval XXI |
Abbreviated Journal |
|
|
| |
Volume |
9021 |
Issue |
|
Pages |
|
|
| |
Keywords |
Document image retrieval; distance transform; MSER; spatial database |
|
| |
Abstract |
The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. |
|
| |
Address |
Amsterdam; September 2014 |
|
| |
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 |
SPIE-DRR |
|
| |
Notes |
DAG; 600.056; 600.061; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GRK2014a |
Serial |
2496 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
|
| |
Title |
Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
Type |
Journal |
| |
Year |
2011 |
Publication |
e-Perimetron |
Abbreviated Journal |
ePER |
|
| |
Volume |
6 |
Issue |
4 |
Pages |
219-229 |
|
| |
Keywords |
|
|
| |
Abstract |
By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput. |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RRL2011a |
Serial |
1765 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
|
| |
Title |
Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
139-141 |
|
| |
Keywords |
Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
|
| |
Abstract |
In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
|
| |
Address |
Bern; Switzerland; September 2015 |
|
| |
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 |
VoteID |
|
| |
Notes |
DAG; 600.061; 602.006; 600.077 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TCP2015 |
Serial |
2641 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas |
|
| |
Title |
OCR-IDL: OCR Annotations for Industry Document Library Dataset |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
ECCV Workshop on Text in Everything |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. |
|
| |
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 |
ECCV |
|
| |
Notes |
DAG; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BTG2022 |
Serial |
3817 |
|
| Permanent link to this record |
