| |
Records |
Links |
|
Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
|
| |
Title |
Learning graph edit distance by graph neural networks |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
120 |
Issue |
|
Pages |
108132 |
|
| |
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 i.e. 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; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RFL2021 |
Serial |
3611 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lei Kang; Pau Riba; Marcal Rusinol; Alicia Fornes; Mauricio Villegas |
|
| |
Title |
Content and Style Aware Generation of Text-line Images for Handwriting Recognition |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. |
|
| |
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; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KRR2021 |
Serial |
3612 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes |
|
| |
Title |
Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
123 |
Issue |
|
Pages |
108370 |
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task. |
|
| |
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; 600.124; 600.121; 602.230 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ JSK2022 |
Serial |
3613 |
|
| 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 |
| |
|
| |
|
Author |
Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
|
| |
Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
International Society for Music Information Retrieval Conference |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
690-696 |
|
| |
Keywords |
|
|
| |
Abstract |
Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. |
|
| |
Address |
Virtual; November 2021 |
|
| |
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 |
ISMIR |
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TBK2021 |
Serial |
3616 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi |
|
| |
Title |
Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
4th International Conference on Historical Cryptology |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
34-37 |
|
| |
Keywords |
|
|
| |
Abstract |
Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering. |
|
| |
Address |
Virtual; September 2021 |
|
| |
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 |
HistoCrypt |
|
| |
Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CSF2021 |
Serial |
3617 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes |
|
| |
Title |
A Transcription Is All You Need: Learning to Align through Attention |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
14th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
| |
Volume |
12916 |
Issue |
|
Pages |
141–146 |
|
| |
Keywords |
|
|
| |
Abstract |
Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset. |
|
| |
Address |
Virtual; 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 |
GREC |
|
| |
Notes |
DAG; 602.230; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TSC2021 |
Serial |
3619 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas |
|
| |
Title |
Multimodal grid features and cell pointers for scene text visual question answering |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
150 |
Issue |
|
Pages |
242-249 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link. |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GBT2021 |
Serial |
3620 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Minesh Mathew; Lluis Gomez; Dimosthenis Karatzas; C.V. Jawahar |
|
| |
Title |
Asking questions on handwritten document collections |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
24 |
Issue |
|
Pages |
235-249 |
|
| |
Keywords |
|
|
| |
Abstract |
This work addresses the problem of Question Answering (QA) on handwritten document collections. Unlike typical QA and Visual Question Answering (VQA) formulations where the answer is a short text, we aim to locate a document snippet where the answer lies. The proposed approach works without recognizing the text in the documents. We argue that the recognition-free approach is suitable for handwritten documents and historical collections where robust text recognition is often difficult. At the same time, for human users, document image snippets containing answers act as a valid alternative to textual answers. The proposed approach uses an off-the-shelf deep embedding network which can project both textual words and word images into a common sub-space. This embedding bridges the textual and visual domains and helps us retrieve document snippets that potentially answer a question. We evaluate results of the proposed approach on two new datasets: (i) HW-SQuAD: a synthetic, handwritten document image counterpart of SQuAD1.0 dataset and (ii) BenthamQA: a smaller set of QA pairs defined on documents from the popular Bentham manuscripts collection. We also present a thorough analysis of the proposed recognition-free approach compared to a recognition-based approach which uses text recognized from the images using an OCR. Datasets presented in this work are available to download at docvqa.org. |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MGK2021 |
Serial |
3621 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
|
| |
Title |
Document Collection Visual Question Answering |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
|
|
| |
Volume |
12822 |
Issue |
|
Pages |
778-792 |
|
| |
Keywords |
Document collection; Visual Question Answering |
|
| |
Abstract |
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. |
|
| |
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 |
ICDAR |
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TKV2021 |
Serial |
3622 |
|
| Permanent link to this record |
