| |
Records |
Links |
|
Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
|
| |
Title |
Graph-Based Deep Generative Modelling for Document Layout Generation |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
12917 |
Issue |
|
Pages |
525-537 |
|
| |
Keywords |
|
|
| |
Abstract |
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices. |
|
| |
Address |
Lausanne; Suissa; September 2021 |
|
| |
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; 600.140; 110.312 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRL2021 |
Serial |
3676 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
|
| |
Title |
Learning Graph Edit Distance by Graph NeuralNetworks |
Type |
Miscellaneous |
| |
Year |
2020 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. |
|
| |
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; 600.121; 600.140; 601.302 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RFL2020 |
Serial |
3555 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
Carlos Boned Riera; Oriol Ramos Terrades |
|
| |
Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2186-2191 |
|
| |
Keywords |
Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
|
| |
Abstract |
Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
|
| |
Address |
Montreal; Quebec; Canada; August 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 |
ICPR |
|
| |
Notes |
DAG; 600.121; 600.162 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BoR2022 |
Serial |
3741 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
|
| |
Title |
Improving Handwritten Music Recognition through Language Model Integration |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
4th International Workshop on Reading Music Systems (WoRMS2022) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
42-46 |
|
| |
Keywords |
optical music recognition; historical sources; diversity; music theory; digital humanities |
|
| |
Abstract |
Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
|
| |
Address |
November 18, 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 |
WoRMS |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TBF2022 |
Serial |
3735 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
|
| |
Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
160 |
Issue |
|
Pages |
43-49 |
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
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; 602.230 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
|
| 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 |
Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl |
|
| |
Title |
A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
ACM Journal on Computing and Cultural Heritage |
Abbreviated Journal |
JOCCH |
|
| |
Volume |
15 |
Issue |
4 |
Pages |
1-18 |
|
| |
Keywords |
|
|
| |
Abstract |
Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
ACM |
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; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SBC2023 |
Serial |
3732 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
|
| |
Title |
DocEnTr: An End-to-End Document Image Enhancement Transformer |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1699-1705 |
|
| |
Keywords |
Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
|
| |
Abstract |
Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
|
| |
Address |
August 21-25, 2022 , Montréal Québec |
|
| |
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 |
ICPR |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SBJ2022 |
Serial |
3730 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli |
|
| |
Title |
A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) |
Abbreviated Journal |
|
|
| |
Volume |
13639 |
Issue |
|
Pages |
3-12 |
|
| |
Keywords |
N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections |
|
| |
Abstract |
Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction. |
|
| |
Address |
December 04 – 07, 2022; Hyderabad, India |
|
| |
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 |
ICFHR |
|
| |
Notes |
DAG; 600.121; 600.162; 602.230; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GBS2022 |
Serial |
3733 |
|
| Permanent link to this record |
