| |
Records |
Links |
|
Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
|
| |
Title  |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
129 |
Issue |
|
Pages |
108766 |
|
| |
Keywords |
|
|
| |
Abstract |
The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
|
| |
Address |
Sept. 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 |
|
|
| |
Notes |
DAG; 600.121; 600.162 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRR2022 |
Serial |
3556 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi |
|
| |
Title |
Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
|
| |
Volume |
7887 |
Issue |
|
Pages |
133-140 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. |
|
| |
Address |
Madeira; Portugal; June 2013 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
IbPRIA |
|
| |
Notes |
DAG; 605.203 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ACS2013 |
Serial |
2328 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |
|
| |
Title |
Overview of DocILE 2023: Document Information Localization and Extraction |
Type |
Conference Article |
| |
Year |
2023 |
Publication |
International Conference of the Cross-Language Evaluation Forum for European Languages |
Abbreviated Journal |
|
|
| |
Volume |
14163 |
Issue |
|
Pages |
276–293 |
|
| |
Keywords |
Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding |
|
| |
Abstract |
This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. |
|
| |
Address |
Thessaloniki; Greece; September 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 |
CLEF |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SUS2023a |
Serial |
3924 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas |
|
| |
Title |
Out-of-Vocabulary Challenge Report |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Proceedings European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
13804 |
Issue |
|
Pages |
359–375 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions. |
|
| |
Address |
Tel-Aviv; Israel; October 2022 |
|
| |
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 |
ECCVW |
|
| |
Notes |
DAG; 600.155; 302.105; 611.002 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GMB2022 |
Serial |
3771 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |
|
| |
Title |
Optimal Linear Combination for Two-class Classifiers |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
Proceedings of the International Conference on Advances in Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Kolkata (India) |
|
| |
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 |
ICAPR |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
DAG @ dag @ RTV2007a |
Serial |
894 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
|
| |
Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
31 |
Issue |
9 |
Pages |
1630–1644 |
|
| |
Keywords |
|
|
| |
Abstract |
The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
|
| |
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 |
0162-8828 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
DAG @ dag @ RVT2009 |
Serial |
1220 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
|
| |
Title |
Optical Music Recognition by Recurrent Neural Networks |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
25-26 |
|
| |
Keywords |
Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
|
| |
Abstract |
Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level |
|
| |
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 @ BRC2017 |
Serial |
3056 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
|
| |
Title |
Optical Music Recognition by Long Short-Term Memory Networks |
Type |
Book Chapter |
| |
Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
Abbreviated Journal |
|
|
| |
Volume |
11009 |
Issue |
|
Pages |
81-95 |
|
| |
Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
|
| |
Abstract |
Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer |
Place of Publication |
|
Editor |
A. Fornes, B. Lamiroy |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-3-030-02283-9 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
GREC |
|
| |
Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
|
| 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 |
Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados |
|
| |
Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Document Analysis |
|
| |
Abstract |
Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. |
|
| |
Address |
Virtual; January 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 |
WACV |
|
| |
Notes |
DAG; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SBD2022 |
Serial |
3615 |
|
| Permanent link to this record |
