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Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
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2014 |
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Manuscript Cultures |
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7 |
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47-58 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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DAG; 600.061; 600.077 |
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3190 |
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Nibal Nayef; Yash Patel; Michal Busta; Pinaki Nath Chowdhury; Dimosthenis Karatzas; Wafa Khlif; Jiri Matas; Umapada Pal; Jean-Christophe Burie; Cheng-lin Liu; Jean-Marc Ogier |
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Title |
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019 |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1582-1587 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge. |
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Sydney; Australia; September 2019 |
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ICDAR |
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DAG; 600.121; 600.129 |
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Admin @ si @ NPB2019 |
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3341 |
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Author |
Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning |
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2019 |
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13th IAPR International Workshop on Graphics Recognition |
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80-85 |
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Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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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Sydney; Australia; September 2019 |
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GREC |
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DAG; 600.140; 601.302; 600.121 |
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Admin @ si @ BRF2019 |
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3354 |
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Author |
Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados |
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Title |
Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes |
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2024 |
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IEEE International Conference on Robotics and Automation in PACIFICO |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
When used in a real-world noisy environment, the capacity to generalize to multiple domains is essential for any autonomous scene text spotting system. However, existing state-of-the-art methods employ pretraining and fine-tuning strategies on natural scene datasets, which do not exploit the feature interaction across other complex domains. In this work, we explore and investigate the problem of domain-agnostic scene text spotting, i.e., training a model on multi-domain source data such that it can directly generalize to target domains rather than being specialized for a specific domain or scenario. In this regard, we present the community a text spotting validation benchmark called Under-Water Text (UWT) for noisy underwater scenes to establish an important case study. Moreover, we also design an efficient super-resolution based end-to-end transformer baseline called DA-TextSpotter which achieves comparable or superior performance over existing text spotting architectures for both regular and arbitrary-shaped scene text spotting benchmarks in terms of both accuracy and model efficiency. The dataset, code and pre-trained models will be released upon acceptance. |
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Yokohama; Japan; May 2024 |
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ICRA |
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DAG |
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Admin @ si @ DBP2024 |
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3979 |
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Author |
Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
End-to-End Handwritten Text Detection and Transcription in Full Pages |
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Conference Article |
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2019 |
Publication |
2nd International Workshop on Machine Learning |
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5 |
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29-34 |
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Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
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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Sydney; Australia; September 2019 |
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ICDAR WML |
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DAG; 600.140; 601.311; 600.140 |
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Admin @ si @ CMV2019 |
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3353 |
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Author |
Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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399-404 |
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Named entity recognition; Handwritten Text Recognition; neural networks |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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Vienna; Austria; April 2018 |
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DAS |
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DAG; 600.097; 603.057; 601.311; 600.121 |
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Admin @ si @ CVF2018 |
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3170 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
A probabilistic framework for handwritten text line segmentation |
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Miscellaneous |
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2018 |
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Arxiv |
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Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step. |
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DAG; 600.097; 600.121 |
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Admin @ si @ CrR2018 |
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3253 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
Application of deformable template matching to symbol recognition in hand-written architectural draw |
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1999 |
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Proceedings of the Fifth International Conference on |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified. |
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Bangalore (India) |
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IAM @ iam @ VAM1999a |
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1657 |
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Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Handwritten Word Spotting with Corrected Attributes |
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Conference Article |
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2013 |
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15th IEEE International Conference on Computer Vision |
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1017-1024 |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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DAG |
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Admin @ si @ AGF2013 |
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2327 |
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Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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Abstract ![sorted by Abstract field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
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Kyoto; Japan; November 2017 |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JRL2017b |
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3073 |
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