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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
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Publisher Place of Publication Editor
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Area Expedition Conference ICDAR
Notes (down) DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ BRC2017 Serial 3056
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Author Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes
Title Graph-based deep learning for graphics classification Type Conference Article
Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 29-30
Keywords
Abstract Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems
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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 (down) DAG; 600.097; 601.302; 600.121 Approved no
Call Number Admin @ si @ RDL2017b Serial 3058
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Author Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados
Title ICDAR2017 Competition on Information Extraction in Historical Handwritten Records Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1389-1394
Keywords
Abstract The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results.
Address Kyoto; Japan; November 2017
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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 (down) DAG; 600.097; 601.225; 600.121 Approved no
Call Number Admin @ si @ FRB2017 Serial 3052
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Author Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados
Title Handwriting Recognition by Attribute embedding and Recurrent Neural Networks Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1038-1043
Keywords
Abstract Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes (down) DAG; 600.097; 601.225; 600.121 Approved no
Call Number Admin @ si @ TDF2017 Serial 3055
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Author Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes
Title In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora Type Journal
Year 2019 Publication Journal for Information Technology Studies as a Human Science Abbreviated Journal HUMAN IT
Volume 14 Issue 2 Pages 95-120
Keywords Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts
Abstract In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples.
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Notes (down) DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ MVH2019 Serial 3234
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Author Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone
Title DSD: document sparse-based denoising algorithm Type Journal Article
Year 2019 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 22 Issue 1 Pages 177–186
Keywords Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models
Abstract In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising.
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Notes (down) DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ DRT2019 Serial 3254
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Author Arnau Baro; Jialuo Chen; Alicia Fornes; Beata Megyesi
Title Towards a generic unsupervised method for transcription of encoded manuscripts Type Conference Article
Year 2019 Publication 3rd International Conference on Digital Access to Textual Cultural Heritage Abbreviated Journal
Volume Issue Pages 73-78
Keywords A. Baró, J. Chen, A. Fornés, B. Megyesi.
Abstract Historical ciphers, a special type of manuscripts, contain encrypted information, important for the interpretation of our history. The first step towards decipherment is to transcribe the images, either manually or by automatic image processing techniques. Despite the improvements in handwritten text recognition (HTR) thanks to deep learning methodologies, the need of labelled data to train is an important limitation. Given that ciphers often use symbol sets across various alphabets and unique symbols without any transcription scheme available, these supervised HTR techniques are not suitable to transcribe ciphers. In this paper we propose an un-supervised method for transcribing encrypted manuscripts based on clustering and label propagation, which has been successfully applied to community detection in networks. We analyze the performance on ciphers with various symbol sets, and discuss the advantages and drawbacks compared to supervised HTR methods.
Address Brussels; May 2019
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Area Expedition Conference DATeCH
Notes (down) DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ BCF2019 Serial 3276
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Author Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados
Title Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework Type Conference Article
Year 2018 Publication 14th Asian Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.
Address Perth; Australia; December 2018
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Publisher Place of Publication Editor
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Area Expedition Conference ACCV
Notes (down) DAG; 600.097; 600.121; 600.129 Approved no
Call Number Admin @ si @ DDG2018a Serial 3151
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Author Marçal Rusiñol; Josep Llados
Title Flowchart Recognition in Patent Information Retrieval Type Book Chapter
Year 2017 Publication Current Challenges in Patent Information Retrieval Abbreviated Journal
Volume 37 Issue Pages 351-368
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor M. Lupu; K. Mayer; N. Kando; A.J. Trippe
Language Summary Language Original Title
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Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ RuL2017 Serial 2896
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Author Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel
Title Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition Type Book Chapter
Year 2017 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages
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Abstract
Address
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Publisher Springer Place of Publication Editor B. Lamiroy; R Dueire Lins
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 (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JLR2017 Serial 2928
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines
Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.
Address Faro; Portugal; June 2017
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Area Expedition Conference IbPRIA
Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017a Serial 2953
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Author Lasse Martensson; Anders Hast; Alicia Fornes
Title Word Spotting as a Tool for Scribal Attribution Type Conference Article
Year 2017 Publication 2nd Conference of the association of Digital Humanities in the Nordic Countries Abbreviated Journal
Volume Issue Pages 87-89
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Abstract
Address Gothenburg; Suecia; March 2017
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-91-88348-83-8 Medium
Area Expedition Conference DHN
Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ MHF2017 Serial 2954
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados
Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.
Address Kyoto; Japan; November 2017
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Publisher Place of Publication Editor
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Area Expedition Conference GREC
Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ JRL2017b Serial 3073
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Author Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal
Title Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario Type Conference Article
Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 31-32
Keywords
Abstract One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration
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Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ DDL2017 Serial 3057
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Author Alicia Fornes; Beata Megyesi; Joan Mas
Title Transcription of Encoded Manuscripts with Image Processing Techniques Type Conference Article
Year 2017 Publication Digital Humanities Conference Abbreviated Journal
Volume Issue Pages 441-443
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Abstract
Address
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Area Expedition Conference DH
Notes (down) DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ FMM2017 Serial 3061
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