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Author |
L.Tarazon; D. Perez; N. Serrano; V. Alabau; Oriol Ramos Terrades; A. Sanchis; A. Juan |
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Title |
Confidence Measures for Error Correction in Interactive Transcription of Handwritten Text |
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Conference Article |
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2009 |
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15th International Conference on Image Analysis and Processing |
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5716 |
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567-574 |
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An effective approach to transcribe old text documents is to follow an interactive-predictive paradigm in which both, the system is guided by the human supervisor, and the supervisor is assisted by the system to complete the transcription task as efficiently as possible. In this paper, we focus on a particular system prototype called GIDOC, which can be seen as a first attempt to provide user-friendly, integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. More specifically, we focus on the handwriting recognition part of GIDOC, for which we propose the use of confidence measures to guide the human supervisor in locating possible system errors and deciding how to proceed. Empirical results are reported on two datasets showing that a word error rate not larger than a 10% can be achieved by only checking the 32% of words that are recognised with less confidence. |
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Vietri sul Mare, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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DAG |
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Admin @ si @ TPS2009 |
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1871 |
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Author |
Raul Gomez; Jaume Gibert; Lluis Gomez; Dimosthenis Karatzas |
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Title |
Location Sensitive Image Retrieval and Tagging |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.121; 600.129 |
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Admin @ si @ GGG2020b |
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3420 |
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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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2020 |
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16th European Conference on Computer Vision |
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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. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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Admin @ si @ KPW2020 |
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3426 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
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Conference Article |
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Year |
2009 |
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16th IEEE International Conference on Image Processing |
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1985-1988 |
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Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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MILAB;HuPBA;DAG |
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no |
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BCNPCL @ bcnpcl @ EFP2009b |
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1184 |
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Author |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
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Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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Volume |
12822 |
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306-320 |
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This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. |
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Lausanne; Suissa; September 2021 |
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ICDAR |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ MRG2021b |
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3571 |
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Author |
Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
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Title |
Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12822 |
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381–395 |
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In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ RMG2021 |
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3572 |
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Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
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Title |
DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12823 |
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555–568 |
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Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ BRL2021a |
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3573 |
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Author |
Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
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Title |
Document Collection Visual Question Answering |
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Conference Article |
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Year |
2021 |
Publication |
16th International Conference on Document Analysis and Recognition |
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12822 |
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778-792 |
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Document collection; Visual Question Answering |
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Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task. |
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DAG; 600.121 |
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Admin @ si @ TKV2021 |
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3622 |
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Author |
Ruben Tito; Minesh Mathew; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas |
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Title |
ICDAR 2021 Competition on Document Visual Question Answering |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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635-649 |
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In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5, 000 infographics images and 30, 000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented. |
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VIRTUAL; Lausanne; Suissa; September 2021 |
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DAG; 600.121 |
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Admin @ si @ TMJ2021 |
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3624 |
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Author |
Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados |
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Title |
A Self-supervised Inverse Graphics Approach for Sketch Parametrization |
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Conference Article |
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Year |
2021 |
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16th International Conference on Document Analysis and Recognition |
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12916 |
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28-42 |
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The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121 |
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Admin @ si @ SRR2021 |
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3675 |
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