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| Author | Minesh Mathew; Viraj Bagal; Ruben Tito; Dimosthenis Karatzas; Ernest Valveny; C.V. Jawahar | ||||
| Title | InfographicVQA | Type | Conference Article | ||
| Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1697-1706 | ||
| Keywords | Document Analysis Datasets; Evaluation and Comparison of Vision Algorithms; Vision and Languages | ||||
| Abstract | Infographics communicate information using a combination of textual, graphical and visual elements. This work explores the automatic understanding of infographic images by using a Visual Question Answering technique. To this end, we present InfographicVQA, a new dataset comprising a diverse collection of infographics and question-answer annotations. The questions require methods that jointly reason over the document layout, textual content, graphical elements, and data visualizations. We curate the dataset with an emphasis on questions that require elementary reasoning and basic arithmetic skills. For VQA on the dataset, we evaluate two Transformer-based strong baselines. Both the baselines yield unsatisfactory results compared to near perfect human performance on the dataset. The results suggest that VQA on infographics--images that are designed to communicate information quickly and clearly to human brain--is ideal for benchmarking machine understanding of complex document images. The dataset is available for download at docvqa. org | ||||
| Address | Virtual; Waikoloa; Hawai; USA; January 2022 | ||||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | WACV | ||
Notes  |
DAG; 600.155 | Approved | no | ||
| Call Number | MBT2022 | Serial | 3625 | ||
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| Author | Anjan Dutta; Zeynep Akata | ||||
| Title | Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | 32nd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 5089-5098 | ||
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| Abstract | Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. | ||||
| Address | Long beach; California; USA; June 2019 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes |
DAG; 600.141; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DuA2019 | Serial | 3268 | ||
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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 | ||
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| 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. |
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| Address | Virtual; June 2020 | ||||
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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 | HistoCrypt | ||
| Notes |
DAG; 600.140; 602.230; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CSF2020 | Serial | 3447 | ||
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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. |
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| Address | Sydney; Australia; September 2019 | ||||
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| 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 | ||
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| Author | Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados | ||||
| Title | A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 219-227 | |
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| Abstract | In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. | ||||
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| Notes |
DAG; 600.140; 601.311; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CFV2020 | Serial | 3451 | ||
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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 | ||
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| 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. |
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| 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 | ||
| Notes |
DAG; 600.140; 601.302; 602.167; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ RDG2019 | Serial | 3355 | ||
| Permanent link to this record | |||||
| 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. |
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| Area | Expedition | Conference | |||
| Notes |
DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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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. |
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| 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 | ||
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| Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
| Title | Recurrent Comparator with attention models to detect counterfeit documents | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation. | ||||
| Address | Sidney; Australia; September 2019 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes |
DAG; 600.140; 600.121; 601.269 | Approved | no | ||
| Call Number | Admin @ si @ BRL2019 | Serial | 3456 | ||
| 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 | |
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| 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 | Helena Muñoz; Fernando Vilariño; Dimosthenis Karatzas | ||||
| Title | Eye-Movements During Information Extraction from Administrative Documents | Type | Conference Article | ||
| Year | 2019 | Publication | International Conference on Document Analysis and Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 6-9 | ||
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| Abstract | A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information. | ||||
| Address | Sydney; Australia; September 2019 | ||||
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| Area | Expedition | Conference | ICDARW | ||
| Notes |
DAG; 600.140; 600.121; 600.129;SIAI | Approved | no | ||
| Call Number | Admin @ si @ MVK2019 | Serial | 3336 | ||
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| Author | Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
| Title | GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes |
DAG; 600.140; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ KPW2020 | Serial | 3426 | ||
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| Author | Sounak Dey; Pau Riba; Anjan Dutta; Josep Llados; Yi-Zhe Song | ||||
| Title | Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2179-2188 | ||
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| Abstract | In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research. | ||||
| Address | Long beach; CA; USA; June 2019 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes |
DAG; 600.140; 600.121; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ DRD2019 | Serial | 3462 | ||
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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 | |
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| 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 | 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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