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| Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
| Title | Learning graph edit distance by graph neural networks | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 120 | Issue | Pages | 108132 | |
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| Abstract | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. | ||||
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DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RFL2021 | Serial | 3611 | ||
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| Author | Lei Kang; Pau Riba; Marcal Rusinol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Content and Style Aware Generation of Text-line Images for Handwriting Recognition | Type | Journal Article | ||
| Year | 2021 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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| Abstract | Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. | ||||
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| Notes |
DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRR2021 | Serial | 3612 | ||
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| Author | Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes | ||||
| Title | On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2021 | Publication | International Society for Music Information Retrieval Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 690-696 | ||
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| Abstract | Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. | ||||
| Address | Virtual; November 2021 | ||||
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| Area | Expedition | Conference | ISMIR | ||
| Notes |
DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ TBK2021 | Serial | 3616 | ||
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| Author | Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
| Title | A Generic Image Retrieval Method for Date Estimation of Historical Document Collections | Type | Conference Article | ||
| Year | 2022 | Publication | Document Analysis Systems.15th IAPR International Workshop, (DAS2022) | Abbreviated Journal | |
| Volume | 13237 | Issue | Pages | 583–597 | |
| Keywords | Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG | ||||
| Abstract | Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. | ||||
| Address | La Rochelle, France; May 22–25, 2022 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes |
DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MGR2022 | Serial | 3694 | ||
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| Author | Souhail Bakkali; Zuheng Ming; Mickael Coustaty; Marçal Rusiñol; Oriol Ramos Terrades | ||||
| Title | VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification | Type | Journal Article | ||
| Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 139 | Issue | Pages | 109419 | |
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| Abstract | Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets. | ||||
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| ISSN | ISSN 0031-3203 | ISBN | Medium | ||
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| Notes |
DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BMC2023 | Serial | 3826 | ||
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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 | 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 | 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 | 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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DAG; 600.140; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ DRL2020 | Serial | 3348 | ||
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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 | |
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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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| Area | Expedition | Conference | ICDAR | ||
| Notes |
DAG; 600.140; 600.121; 601.269 | Approved | no | ||
| Call Number | Admin @ si @ BRL2019 | Serial | 3456 | ||
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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 | ||||
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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 | 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 | |
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| 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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DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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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 | ||||
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| Area | Expedition | Conference | ICDAR | ||
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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 | 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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DAG; 600.140; 601.311; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CFV2020 | Serial | 3451 | ||
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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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| 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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