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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title  |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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Book Chapter |
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2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
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Title |
Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique |
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Conference Article |
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Year |
2012 |
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Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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7626 |
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243-253 |
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Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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DAG |
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Admin @ si @ LRL2012 |
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2381 |
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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 |
Publication |
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 |
Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen |
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Title |
Improving HMM-Based Keyword Spotting with Character Language Models |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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506-510 |
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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. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 605.203 |
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Admin @ si @ FFB2013 |
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2295 |
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Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes; Sounak Dey |
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Title |
Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms |
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Conference Article |
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Year |
2017 |
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14th International Conference on Document Analysis and Recognition |
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475-480 |
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document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion |
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Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the
state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RDL2017a |
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3053 |
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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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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 |
Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Improving Text Proposals for Scene Images with Fully Convolutional Networks |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art. |
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Cancun; Mexico; December 2016 |
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ICPRW |
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DAG; LAMP; 600.084 |
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Admin @ si @ BGN2016 |
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2823 |
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Author |
Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes |
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Title |
In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora |
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2019 |
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Journal for Information Technology Studies as a Human Science |
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HUMAN IT |
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14 |
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2 |
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95-120 |
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Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts |
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In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples. |
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DAG; 600.097; 600.140; 600.121 |
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Admin @ si @ MVH2019 |
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3234 |
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Author |
Josep Llados; Gemma Sanchez |
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Title |
Indexing Historical Documents by Word Shape Signatures |
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2007 |
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9th International Conference on Document Analysis and Recognition |
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1 |
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362–366 |
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Curitiba (Brasil) |
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DAG @ dag @ LlS2007 |
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882 |
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Oriol Ramos Terrades; Ernest Valveny |
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Indexing Technical Symbols Using Ridgelets Transform |
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Miscellaneous |
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2003 |
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Proceedings of the Fifth International Workshop on Graphics Recognition (GREC´03), 202–211 |
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DAG @ dag @ RaV2003c |
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