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| Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
| Title | The Library Living Lab Barcelona: A participative approach to technology as an enabling factor for innovation in cultural spaces | Type | Journal | ||
| Year | 2018 | Publication | Technology Innovation Management Review | Abbreviated Journal | |
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| Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved  |
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| Call Number | Admin @ si @ VKV2018a | Serial | 3153 | ||
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| Author | Fernando Vilariño; Dimosthenis Karatzas; Alberto Valcarce | ||||
| Title | Libraries as New Innovation Hubs: The Library Living Lab | Type | Conference Article | ||
| Year | 2018 | Publication | 30th ISPIM Innovation Conference | Abbreviated Journal | |
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| Abstract | Libraries are in deep transformation both in EU and around the world, and they are thriving within a great window of opportunity for innovation. In this paper, we show how the Library Living Lab in Barcelona participated of this changing scenario and contributed to create the Bibliolab program, where more than 200 public libraries give voice to their users in a global user-centric innovation initiative, using technology as enabling factor. The Library Living Lab is a real 4-helix implementation where Universities, Research Centers, Public Administration, Companies and the Neighbors are joint together to explore how technology transforms the cultural experience of people. This case is an example of scalability and provides reference tools for policy making, sustainability, user engage methodologies and governance. We provide specific examples of new prototypes and services that help to understand how to redefine the role of the Library as a real hub for social innovation. | ||||
| Address | Stockholm; May 2018 | ||||
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| Area | Expedition | Conference | ISPIM | ||
| Notes | DAG; MV; 600.097; 600.121; 600.129;SIAI | Approved |
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| Call Number | Admin @ si @ VKV2018b | Serial | 3154 | ||
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| Author | Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio | ||||
| Title | Image-to-image translation for cross-domain disentanglement | Type | Conference Article | ||
| Year | 2018 | Publication | 32nd Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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| Address | Montreal; Canada; December 2018 | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | LAMP; 600.120 | Approved |
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| Call Number | Admin @ si @ GWB2018 | Serial | 3155 | ||
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| Author | Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez | ||||
| Title | Metric Learning for Novelty and Anomaly Detection | Type | Conference Article | ||
| Year | 2018 | Publication | 29th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. | ||||
| Address | Newcastle; uk; September 2018 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 | Approved |
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| Call Number | Admin @ si @ MRS2018 | Serial | 3156 | ||
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| Author | Marco Buzzelli; Joost Van de Weijer; Raimondo Schettini | ||||
| Title | Learning Illuminant Estimation from Object Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 25th International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 3234 - 3238 | ||
| Keywords | Illuminant estimation; computational color constancy; semi-supervised learning; deep learning; convolutional neural networks | ||||
| Abstract | In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep
learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions. |
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| Address | Athens; Greece; October 2018 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | LAMP; 600.109; 600.120 | Approved |
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| Call Number | Admin @ si @ BWS2018 | Serial | 3157 | ||
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| Author | Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen | ||||
| Title | Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification | Type | Journal Article | ||
| Year | 2018 | Publication | ISPRS Journal of Photogrammetry and Remote Sensing | Abbreviated Journal | ISPRS J |
| Volume | 138 | Issue | Pages | 74-85 | |
| Keywords | Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis | ||||
| Abstract | Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene | ||||
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| Notes | LAMP; 600.109; 600.106; 600.120 | Approved |
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| Call Number | Admin @ si @ RKW2018 | Serial | 3158 | ||
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| Author | Xialei Liu; Joost Van de Weijer; Andrew Bagdanov | ||||
| Title | Leveraging Unlabeled Data for Crowd Counting by Learning to Rank | Type | Conference Article | ||
| Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 7661 - 7669 | ||
| Keywords | Task analysis; Training; Computer vision; Visualization; Estimation; Head; Context modeling | ||||
| Abstract | We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of
cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-ofthe-art results. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.109; 600.106; 600.120 | Approved |
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| Call Number | Admin @ si @ LWB2018 | Serial | 3159 | ||
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| Author | Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov | ||||
| Title | Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2262-2268 | ||
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| Abstract | In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting. |
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 | Approved |
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| Call Number | Admin @ si @ LMH2018 | Serial | 3160 | ||
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| Author | Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa | ||||
| Title | Color Naming for Multi-Color Fashion Items | Type | Conference Article | ||
| Year | 2018 | Publication | 6th World Conference on Information Systems and Technologies | Abbreviated Journal | |
| Volume | 747 | Issue | Pages | 64-73 | |
| Keywords | Deep learning; Color; Multi-label | ||||
| Abstract | There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. | ||||
| Address | Naples; March 2018 | ||||
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| Area | Expedition | Conference | WORLDCIST | ||
| Notes | LAMP; 600.109; 601.309; 600.120 | Approved |
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| Call Number | Admin @ si @ YWR2018 | Serial | 3161 | ||
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| Author | Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy | ||||
| Title | On Offline Evaluation of Vision-based Driving Models | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 11219 | Issue | Pages | 246-262 | |
| Keywords | Autonomous driving; deep learning | ||||
| Abstract | Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. |
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| Address | Munich; September 2018 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | ADAS; 600.124; 600.118 | Approved |
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| Call Number | Admin @ si @ CLK2018 | Serial | 3162 | ||
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| Author | Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez | ||||
| Title | GTCreator: a flexible annotation tool for image-based datasets | Type | Journal Article | ||
| Year | 2019 | Publication | International Journal of Computer Assisted Radiology and Surgery | Abbreviated Journal | IJCAR |
| Volume | 14 | Issue | 2 | Pages | 191–201 |
| Keywords | Annotation tool; Validation Framework; Benchmark; Colonoscopy; Evaluation | ||||
| Abstract | Abstract Purpose: Methodology evaluation for decision support systems for health is a time consuming-task. To assess performance of polyp detection
methods in colonoscopy videos, clinicians have to deal with the annotation of thousands of images. Current existing tools could be improved in terms of exibility and ease of use. Methods:We introduce GTCreator, a exible annotation tool for providing image and text annotations to image-based datasets. It keeps the main basic functionalities of other similar tools while extending other capabilities such as allowing multiple annotators to work simultaneously on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. Results: The comparison with other similar tools shows that GTCreator allows to obtain fast and precise annotation of image datasets, being the only one which offers full annotation editing and browsing capabilites. Conclusions: Our proposed annotation tool has been proven to be efficient for large image dataset annota- tion, as well as showing potential of use in other stages of method evaluation such as experimental setup or results analysis. |
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| Notes | MV; 600.096; 600.109; 600.119; 601.305 | Approved |
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| Call Number | Admin @ si @ BHM2019 | Serial | 3163 | ||
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| Author | Cristina Sanchez Montes; F. Javier Sanchez; Jorge Bernal; Henry Cordova; Maria Lopez Ceron; Miriam Cuatrecasas; Cristina Rodriguez de Miguel; Ana Garcia Rodriguez; Rodrigo Garces Duran; Maria Pellise; Josep Llach; Gloria Fernandez Esparrach | ||||
| Title | Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis | Type | Journal Article | ||
| Year | 2019 | Publication | Endoscopy | Abbreviated Journal | END |
| Volume | 51 | Issue | 3 | Pages | 261-265 |
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| Abstract | Background and study aims: To evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images.
Patients and methods: Textural elements (textons) were characterized according to their contrast with respect to the surface, shape and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis by the endoscopists using Kudo and NICE classification. Results: Images of 225 polyps were evaluated (142 dysplastic and 83 non-dysplastic). CAD system correctly classified 205 (91.1%) polyps, 131/142 (92.3%) dysplastic and 74/83 (89.2%) non-dysplastic. For the subgroup of 100 diminutive (<5 mm) polyps, CAD correctly classified 87 (87%) polyps, 43/50 (86%) dysplastic and 44/50 (88%) non-dysplastic. There were not statistically significant differences in polyp histology prediction based on CAD system and on endoscopist assessment. Conclusion: A computer vision system based on the characterization of the polyp surface in the white light accurately predicts colorectal polyp histology. |
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| Notes | MV; 600.096; 600.119; 600.075 | Approved |
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| Call Number | Admin @ si @ SSB2019 | Serial | 3164 | ||
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| Author | F. Javier Sanchez; Jorge Bernal | ||||
| Title | Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject | Type | Conference Article | ||
| Year | 2018 | Publication | 4th International Conference of Higher Education Advances | Abbreviated Journal | |
| Volume | Issue | Pages | 1359-1366 | ||
| Keywords | Monitoring; Evaluation tool; Gamification; Student motivation | ||||
| Abstract | The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject. | ||||
| Address | Valencia; June 2018 | ||||
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| Area | Expedition | Conference | HEAD | ||
| Notes | MV; no proj | Approved |
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| Call Number | Admin @ si @ SaB2018 | Serial | 3165 | ||
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| Author | Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados | ||||
| Title | Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model | Type | Journal Article | ||
| Year | 2019 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 86 | Issue | Pages | 27-36 | |
| Keywords | Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks | ||||
| Abstract | Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. | ||||
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| Notes | DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 | Approved |
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| Call Number | Admin @ si @ TCF2019 | Serial | 3166 | ||
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| Author | Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol | ||||
| Title | Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 40th German Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 459-472 | ||
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| Abstract | This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR. | ||||
| Address | Stuttgart; Germany; October 2018 | ||||
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| Area | Expedition | Conference | GCPR | ||
| Notes | DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 | Approved |
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| Call Number | Admin @ si @ KTR2018 | Serial | 3167 | ||
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