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Author Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol
Title Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture Type Journal Article
Year 2021 Publication Pattern Recognition Abbreviated Journal PR
Volume 112 Issue Pages 107790
Keywords
Abstract Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging
problem. The main challenge faced when training a language model is to
deal with the language model corpus which is usually different to the one
used for training the handwritten word recognition system. Thus, the bias
between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this
work, we introduce Candidate Fusion, a novel way to integrate an external
language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to
the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two
improvements. On the one hand, the sequence-to-sequence recognizer has
the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided
by the language model. On the other hand, the external language model
has the ability to adapt itself to the training corpus and even learn the
most commonly errors produced from the recognizer. Finally, by conducting
comprehensive experiments, the Candidate Fusion proves to outperform the
state-of-the-art language models for handwritten word recognition tasks.
Address
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Notes DAG; 600.140; 601.302; 601.312; 600.121 Approved no
Call Number Admin @ si @ KRV2021 Serial 3343
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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
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Area Expedition Conference ICFHR
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ BFB2020 Serial 3448
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Author Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov
Title Improved Discrete Optical Flow Estimation With Triple Image Matching Cost Type Journal Article
Year 2020 Publication IEEE Access Abbreviated Journal ACCESS
Volume 8 Issue Pages 17093 - 17102
Keywords
Abstract Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset.
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Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ YCW2020 Serial 3345
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Author Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov
Title Variable Rate Deep Image Compression with Modulated Autoencoder Type Journal Article
Year 2020 Publication IEEE Signal Processing Letters Abbreviated Journal SPL
Volume 27 Issue Pages 331-335
Keywords
Abstract Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.
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Area Expedition Conference
Notes LAMP; ADAS; 600.141; 600.120; 600.118 Approved no
Call Number Admin @ si @ YHW2020 Serial 3346
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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.
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Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ MEF2020 Serial 3347
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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.
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Notes DAG; 600.140; 600.121; 600.141 Approved no
Call Number Admin @ si @ DRL2020 Serial 3348
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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).
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Notes DAG; 600.097; 601.302; 603.057; 600.140; 600.121 Approved no
Call Number Admin @ si @ RLF2020 Serial 3349
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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 (down) 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
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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.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (down) ISBN 978-5-7996-2656-3 Medium
Area Expedition Conference
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ PFL2019 Serial 3351
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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
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference HistoCrypt
Notes DAG; 600.140; 602.230; 600.121 Approved no
Call Number Admin @ si @ CSF2020 Serial 3447
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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
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Area Expedition Conference HIP
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ RGF2019 Serial 3352
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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
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Area Expedition Conference ICDAR WML
Notes DAG; 600.140; 601.311; 600.140 Approved no
Call Number Admin @ si @ CMV2019 Serial 3353
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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
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Area Expedition Conference GREC
Notes DAG; 600.140; 601.302; 600.121 Approved no
Call Number Admin @ si @ BRF2019 Serial 3354
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Author Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados
Title Table Detection in Invoice Documents by Graph Neural Networks Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 122-127
Keywords
Abstract Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
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
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Area Expedition Conference ICDAR
Notes DAG; 600.140; 601.302; 602.167; 600.121; 600.141 Approved no
Call Number Admin @ si @ RDG2019 Serial 3355
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Author Ekta Vats; Anders Hast; Alicia Fornes
Title Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1294-1299
Keywords Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching
Abstract Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
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 (down) ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ VHF2019 Serial 3356
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