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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |
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Title |
Performance Evaluation of Symbol Recognition and Spotting Systems |
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Conference Article |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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497–505 |
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Nara (Japan) |
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DAS |
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DAG @ dag @ DVL2008b |
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1060 |
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Author |
Joan Mas; Jose Antonio Rodriguez; Dimosthenis Karatzas; Gemma Sanchez; Josep Llados |
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Title |
HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents |
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Conference Article |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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517–524 |
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Nara (Japan) |
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DAG @ dag @ MRK2008a |
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1061 |
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Author |
Dimosthenis Karatzas |
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Title |
Detecting Gradients in Text Images Using the Hough Transform |
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Conference Article |
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2008 |
Publication |
Proceedings of the 8th International Workshop on Document Analysis Systems, |
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245–252 |
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Nara (Japan) |
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DAG @ dag @ Kar2008 |
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1062 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
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Title |
Writer Identification in Old Handwritten Music Scores |
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Conference Article |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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347–353 |
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Nara (Japan) |
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DAS |
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DAG |
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DAG @ dag @ FLS2008b |
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1078 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Multi-oriented English Text Line Extraction using Background and Foreground Information |
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Conference Article |
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2008 |
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Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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315–322 |
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Nara (Japo) |
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DAS |
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DAG |
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DAG @ dag @ RPL2008b |
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1047 |
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Author |
T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval |
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Conference Article |
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Year |
2008 |
Publication |
Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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191-197 |
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Nara, Japan |
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DAS |
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DAG |
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Admin @ si @ NTR2008a |
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1873 |
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Author |
N. Zakaria; Jean-Marc Ogier; Josep Llados |
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Title |
The Fuzzy-Spatial Descriptor for the Online Graphic Recognition: Overlapping Matrix Algorithm |
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Book Chapter |
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Year |
2006 |
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7th International Workshop, Document Analysis Systems VII (DAS´06), LNCS 3872: 616–627 |
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Nelson (New Zealand) |
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DAG |
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DAG @ dag @ ZOL2006 |
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629 |
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Author |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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Year |
2018 |
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16th International Conference on Frontiers in Handwriting Recognition |
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528-533 |
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Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Abstract |
Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
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Admin @ si @ CRF2018 |
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3169 |
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Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
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Title |
Improving Handwritten Music Recognition through Language Model Integration |
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Conference Article |
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Year |
2022 |
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4th International Workshop on Reading Music Systems (WoRMS2022) |
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42-46 |
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optical music recognition; historical sources; diversity; music theory; digital humanities |
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Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
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November 18, 2022 |
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WoRMS |
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DAG; 600.121; 600.162; 602.230 |
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Admin @ si @ TBF2022 |
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3735 |
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Author |
Dena Bazazian |
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Title |
Fully Convolutional Networks for Text Understanding in Scene Images |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging. |
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November 2018 |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Dimosthenis Karatzas;Andrew Bagdanov |
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978-84-948531-1-1 |
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DAG; 600.121 |
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Admin @ si @ Baz2018 |
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3220 |
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