| |
Records |
Links |
|
Author |
Arnau Baro; Alicia Fornes; Carles Badal |
|
| |
Title |
Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
17th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
|
| |
Address |
Virtual ICFHR; September 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 |
ICFHR |
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BFB2020 |
Serial |
3448 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl |
|
| |
Title |
Decryption of historical manuscripts: the DECRYPT project |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Cryptologia |
Abbreviated Journal |
CRYPT |
|
| |
Volume |
44 |
Issue |
6 |
Pages |
545-559 |
|
| |
Keywords |
automatic decryption; cipher collection; historical cryptology; image transcription |
|
| |
Abstract |
Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. |
|
| |
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 @ MEF2020 |
Serial |
3347 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes |
|
| |
Title |
Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Neural Computing and Applications |
Abbreviated Journal |
NEUCOMA |
|
| |
Volume |
32 |
Issue |
|
Pages |
11579–11596 |
|
| |
Keywords |
|
|
| |
Abstract |
Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods. |
|
| |
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; 600.141 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DRL2020 |
Serial |
3348 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Riba; Josep Llados; Alicia Fornes |
|
| |
Title |
Hierarchical graphs for coarse-to-fine error tolerant matching |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
134 |
Issue |
|
Pages |
116-124 |
|
| |
Keywords |
Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval |
|
| |
Abstract |
During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). |
|
| |
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; 600.097; 601.302; 603.057; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RLF2020 |
Serial |
3349 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora |
|
| |
Title |
Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images |
Type |
Book Chapter |
| |
Year |
2020 |
Publication |
Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
World Scientific |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-981-120-323-7 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FLP2020 |
Serial |
3350 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Gabriel Brea-Martinez; Miquel Valls-Figols |
|
| |
Title |
The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries |
Type |
Book Chapter |
| |
Year |
2019 |
Publication |
Nominative Data in Demographic Research in the East and the West: monograph |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
29-61 |
|
| |
Keywords |
|
|
| |
Abstract |
The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially. |
|
| |
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 |
978-5-7996-2656-3 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PFL2019 |
Serial |
3351 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi |
|
| |
Title |
A Web-based Interactive Transcription Tool for Encrypted Manuscripts |
Type |
Conference Article |
| |
Year |
2020 |
Publication |
3rd International Conference on Historical Cryptology |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
52-59 |
|
| |
Keywords |
|
|
| |
Abstract |
Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available. |
|
| |
Address |
Virtual; June 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 |
HistoCrypt |
|
| |
Notes |
DAG; 600.140; 602.230; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CSF2020 |
Serial |
3447 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
|
| |
Title |
Information Extraction in Handwritten Marriage Licenses Books |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
5th International Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
66-71 |
|
| |
Keywords |
|
|
| |
Abstract |
Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach. |
|
| |
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 |
HIP |
|
| |
Notes |
DAG; 600.140; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGF2019 |
Serial |
3352 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados |
|
| |
Title |
End-to-End Handwritten Text Detection and Transcription in Full Pages |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
2nd International Workshop on Machine Learning |
Abbreviated Journal |
|
|
| |
Volume |
5 |
Issue |
|
Pages |
29-34 |
|
| |
Keywords |
Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning |
|
| |
Abstract |
When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately. |
|
| |
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 WML |
|
| |
Notes |
DAG; 600.140; 601.311; 600.140 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CMV2019 |
Serial |
3353 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados |
|
| |
Title |
Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
13th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
80-85 |
|
| |
Keywords |
Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning |
|
| |
Abstract |
With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results. |
|
| |
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 |
GREC |
|
| |
Notes |
DAG; 600.140; 601.302; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRF2019 |
Serial |
3354 |
|
| Permanent link to this record |
