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Author |
Lasse Martensson; Anders Hast; Alicia Fornes |
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Title |
Word Spotting as a Tool for Scribal Attribution |
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Conference Article |
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2017 |
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2nd Conference of the association of Digital Humanities in the Nordic Countries |
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87-89 |
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Gothenburg; Suecia; March 2017 |
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978-91-88348-83-8 |
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DAG; 600.097; 600.121 |
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Admin @ si @ MHF2017 |
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2954 |
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Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
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Journal Article |
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Year |
2017 |
Publication |
Pattern Recognition |
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PR |
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67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GNK2017 |
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2887 |
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Author |
Mathieu Nicolas Delalandre; Tony Pridmore; Ernest Valveny; Eric Trupin; Herve Locteau |
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Title |
Building Synthetic Graphical Documents for Performance Evaluation |
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Conference Article |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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84–87 |
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Curitiba (Brasil) |
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GREC |
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DAG |
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DAG @ dag @ DPV2007 |
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840 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Apostolos Antonacopoulos; Josep Llados |
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Title |
An interactive appearance-based document retrieval system for historical newspapers |
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Conference Article |
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2013 |
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Proceedings of the International Conference on Computer Vision Theory and Applications |
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84-87 |
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In this paper we present a retrieval-based application aimed at assisting a user to semi-automatically segment an incoming flow of historical newspaper images by automatically detecting a particular type of pages based on their appearance. A visual descriptor is used to assess page similarity while a relevance feedback process allow refining the results iteratively. The application is tested on a large dataset of digitised historic newspapers. |
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Barcelona; February 2013 |
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VISAPP |
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DAG; 600.056; 600.045; 605.203 |
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Admin @ si @ GRK2013a |
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2290 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
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Journal Article |
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Year |
2009 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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12 |
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2 |
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83-96 |
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Keywords |
Performance evaluation; Symbol Spotting; Graphics Recognition |
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Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
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1433-2833 |
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DAG |
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DAG @ dag @ RuL2009a |
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1166 |
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Author |
Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke |
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Title |
A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors |
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Conference Article |
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2011 |
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Proceedings of the 2011 Workshop on Historical Document Imaging and Processing |
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83-90 |
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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. |
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ACM |
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978-1-4503-0916-5 |
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HIP |
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DAG |
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Admin @ si @ FFF2011a |
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1823 |
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Author |
Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
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Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
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Conference Article |
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2023 |
Publication |
Document Analysis and Recognition – ICDAR 2023 Workshops |
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14193 |
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83-93 |
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Historical Manuscripts; Symbol Alignment |
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Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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ICDAR |
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DAG |
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Admin @ si @ TSS2023 |
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3850 |
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Author |
Ernest Valveny; Salvatore Tabbone; Oriol Ramos Terrades; Emilie Jean-Marie Odile |
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Title |
Performance Characterization of Shape Descriptors for Symbol Representation |
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Conference Article |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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82–83 |
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Curitiba (Brazil) |
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GREC |
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DAG |
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DAG @ dag @ VTR2007 |
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889 |
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Author |
N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan |
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Title |
The GIDOC Prototype |
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Conference Article |
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2010 |
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10th International Workshop on Pattern Recognition in Information Systems |
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82-89 |
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Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions. |
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Funchal, Portugal |
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978-989-8425-14-0 |
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PRIS |
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DAG |
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Admin @ si @ STP2010 |
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1868 |
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Author |
Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Long Short-Term Memory Networks |
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Book Chapter |
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2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
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11009 |
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81-95 |
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Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
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Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. |
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Springer |
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A. Fornes, B. Lamiroy |
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978-3-030-02283-9 |
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GREC |
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DAG; 600.097; 601.302; 601.330; 600.121 |
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Admin @ si @ BRC2018 |
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3227 |
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