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Author | Lluis Gomez; Dimosthenis Karatzas | ||||
Title | TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild | Type | Journal Article | ||
Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 70 | Issue | Pages | 60-74 | |
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Abstract | Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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Notes | DAG; 600.084; 601.197; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ GoK2017 | Serial | 2886 | ||
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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 | Bogdan Raducanu; Fadi Dornaika | ||||
Title | Embedding new observations via sparse-coding for non-linear manifold learning | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 1 | Pages | 480-492 |
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Abstract | Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. | ||||
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Notes | LAMP; | Approved | no | ||
Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 44 | Issue | 10-11 | Pages | 2414-2416 |
Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Notes | MILAB;HuPBA | Approved | no | ||
Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
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Author | Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre | ||||
Title | Continuous Generalized Procrustes Analysis | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 2 | Pages | 659–671 |
Keywords | Procrustes analysis; 2D shape model; Continuous approach | ||||
Abstract | PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects. To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA). CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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Notes | OR; HuPBA; 605.203; 600.046;MILAB | Approved | no | ||
Call Number | Admin @ si @ IPE2014 | Serial | 2352 | ||
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Author | Parichehr Behjati; Pau Rodriguez; Carles Fernandez; Isabelle Hupont; Armin Mehri; Jordi Gonzalez | ||||
Title | Single image super-resolution based on directional variance attention network | Type | Journal Article | ||
Year | 2023 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 133 | Issue | Pages | 108997 | |
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Abstract | Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neural networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies simultaneously for more discriminative representations. Furthermore, we propose a residual attention feature group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ BPF2023 | Serial | 3861 | ||
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Author | Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu | ||||
Title | Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 80 | Issue | Pages | 64-82 | |
Keywords | Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition | ||||
Abstract | Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. | ||||
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RSJ2018 | Serial | 3096 | ||
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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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Notes | DAG; 600.140; 601.302; 601.312; 600.121 | Approved | no | ||
Call Number | Admin @ si @ KRV2021 | Serial | 3343 | ||
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Author | Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta | ||||
Title | Structure-preserving smoothing of biomedical images | Type | Journal Article | ||
Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 44 | Issue | 9 | Pages | 1842-1851 |
Keywords | Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography | ||||
Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
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ISSN | 0031-3203 | ISBN | Medium | ||
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Notes | IAM; ADAS | Approved | no | ||
Call Number | IAM @ iam @ GHB2011 | Serial | 1526 | ||
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Author | Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 45 | Issue | 4 | Pages | 1627-1636 |
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Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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ISSN | 0031-3203 | ISBN | Medium | ||
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Notes | ISE; CAT;CIC | Approved | no | ||
Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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Author | Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard | ||||
Title | Fuzzy Multilevel Graph Embedding | Type | Journal Article | ||
Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 46 | Issue | 2 | Pages | 551-565 |
Keywords | Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic | ||||
Abstract | Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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ISSN | 0031-3203 | ISBN | Medium | ||
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Notes | DAG; 600.042; 600.045; 605.203 | Approved | no | ||
Call Number | Admin @ si @ LRL2013a | Serial | 2270 | ||
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Author | Pau Riba; Lutz Goldmann; Oriol Ramos Terrades; Diede Rusticus; Alicia Fornes; Josep Llados | ||||
Title | Table detection in business document images by message passing networks | Type | Journal Article | ||
Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 127 | Issue | Pages | 108641 | |
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Abstract | Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches. | ||||
Address | July 2022 | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | DAG; 600.162; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RGR2022 | Serial | 3729 | ||
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Author | Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas | ||||
Title | Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition | Type | Journal Article | ||
Year | 2022 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 129 | Issue | Pages | 108766 | |
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Abstract | The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. | ||||
Address | Sept. 2022 | ||||
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Notes | DAG; 600.121; 600.162 | Approved | no | ||
Call Number | Admin @ si @ KRR2022 | Serial | 3556 | ||
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Author | Susana Alvarez; Maria Vanrell | ||||
Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | ||
Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 45 | Issue | 12 | Pages | 4312-4325 |
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Abstract | The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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ISSN | 0031-3203 | ISBN | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ AlV2012a | Serial | 2130 | ||
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Author | Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke | ||||
Title | Keyword spotting for self-training of BLSTM NN based handwriting recognition systems | Type | Journal Article | ||
Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 47 | Issue | 3 | Pages | 1073-1082 |
Keywords | Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning | ||||
Abstract | The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. | ||||
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Notes | DAG; 600.077; 602.101 | Approved | no | ||
Call Number | Admin @ si @ FFB2014 | Serial | 2297 | ||
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