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Author (up) Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados edit   pdf
openurl 
  Title ICDAR2017 Competition on Information Extraction in Historical Handwritten Records Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1389-1394  
  Keywords  
  Abstract The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results.  
  Address Kyoto; Japan; November 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.097; 601.225; 600.121 Approved no  
  Call Number Admin @ si @ FRB2017 Serial 3052  
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Author (up) Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke edit  doi
isbn  openurl
  Title A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors Type Conference Article
  Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal  
  Volume Issue Pages 83-90  
  Keywords  
  Abstract The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.  
  Address  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4503-0916-5 Medium  
  Area Expedition Conference HIP  
  Notes DAG Approved no  
  Call Number Admin @ si @ FFF2011a Serial 1823  
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Author (up) Alicia Fornes; Xavier Otazu; Josep Llados edit   pdf
doi  openurl
  Title Show through cancellation and image enhancement by multiresolution contrast processing Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 200-204  
  Keywords  
  Abstract Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities.  
  Address Washington; USA; August 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 602.006; 600.045; 600.061; 600.052;CIC Approved no  
  Call Number Admin @ si @ FOL2013 Serial 2241  
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Author (up) Alloy Das; Sanket Biswas; Ayan Banerjee; Josep Llados; Umapada Pal; Saumik Bhattacharya edit   pdf
url  openurl
  Title Harnessing the Power of Multi-Lingual Datasets for Pre-training: Towards Enhancing Text Spotting Performance Type Conference Article
  Year 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 718-728  
  Keywords  
  Abstract The adaptation capability to a wide range of domains is crucial for scene text spotting models when deployed to real-world conditions. However, existing state-of-the-art (SOTA) approaches usually incorporate scene text detection and recognition simply by pretraining on natural scene text datasets, which do not directly exploit the intermediate feature representations between multiple domains. Here, we investigate the problem of domain-adaptive scene text spotting, i.e., training a model on multi-domain source data such that it can directly adapt to target domains rather than being specialized for a specific domain or scenario. Further, we investigate a transformer baseline called Swin-TESTR to focus on solving scene-text spotting for both regular and arbitrary-shaped scene text along with an exhaustive evaluation. The results clearly demonstrate the potential of intermediate representations to achieve significant performance on text spotting benchmarks across multiple domains (e.g. language, synth-to-real, and documents). both in terms of accuracy and efficiency.  
  Address Waikoloa; Hawai; USA; January 2024  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes DAG Approved no  
  Call Number Admin @ si @ DBB2024 Serial 3986  
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Author (up) Alloy Das; Sanket Biswas; Umapada Pal; Josep Llados edit   pdf
url  openurl
  Title Diving into the Depths of Spotting Text in Multi-Domain Noisy Scenes Type Miscellaneous
  Year 2023 Publication ARXIV Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ DBP2023 Serial 3979  
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Author (up) Anders Hast; Alicia Fornes edit   pdf
doi  openurl
  Title A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching Type Conference Article
  Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 150-155  
  Keywords  
  Abstract The automatic recognition of historical handwritten documents is still considered challenging task. For this reason, word spotting emerges as a good alternative for making the information contained in these documents available to the user. Word spotting is defined as the task of retrieving all instances of the query word in a document collection, becoming a useful tool for information retrieval. In this paper we propose a segmentation-free word spotting approach able to deal with large document collections. Our method is inspired on feature matching algorithms that have been applied to image matching and retrieval. Since handwritten words have different shape, there is no exact transformation to be obtained. However, the sufficient degree of relaxation is achieved by using a Fourier based descriptor and an alternative approach to RANSAC called PUMA. The proposed approach is evaluated on historical marriage records, achieving promising results.  
  Address Santorini; Greece; April 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 602.006; 600.061; 600.077; 600.097 Approved no  
  Call Number HaF2016 Serial 2753  
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Author (up) Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai edit   pdf
url  doi
isbn  openurl
  Title Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks Type Conference Article
  Year 2022 Publication 17th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 329–344  
  Keywords  
  Abstract Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-25068-2 Medium  
  Area Expedition Conference ECCV-TiE  
  Notes DAG; 600.162; 600.140; 110.312 Approved no  
  Call Number Admin @ si @ GBC2022 Serial 3795  
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Author (up) Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke edit   pdf
doi  isbn
openurl 
  Title A Fast Matching Algorithm for Graph-Based Handwriting Recognition Type Conference Article
  Year 2013 Publication 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition Abbreviated Journal  
  Volume 7877 Issue Pages 194-203  
  Keywords  
  Abstract The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.  
  Address Vienna; Austria; May 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-38220-8 Medium  
  Area Expedition Conference GBR  
  Notes DAG; 600.045; 605.203 Approved no  
  Call Number Admin @ si @ FSF2013 Serial 2294  
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Author (up) Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke edit  doi
openurl 
  Title Transcription Alignment of Latin Manuscripts Using Hidden Markov Models Type Conference Article
  Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal  
  Volume Issue Pages 29-36  
  Keywords  
  Abstract Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.  
  Address  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference HIP  
  Notes DAG Approved no  
  Call Number Admin @ si @ FFF2011b Serial 1824  
Permanent link to this record
 
 
Author (up) Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen edit   pdf
doi  openurl
  Title Improving HMM-Based Keyword Spotting with Character Language Models Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 506-510  
  Keywords  
  Abstract Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance.  
  Address Washington; USA; August 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.045; 605.203 Approved no  
  Call Number Admin @ si @ FFB2013 Serial 2295  
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