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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part III |
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Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
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12823 |
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Abstract |
This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Lausanne, Switzerland, September 5-10, 2021 |
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Springer Cham |
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Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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978-3-030-86333-3 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ |
Serial |
3727 |
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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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Volume |
12822 |
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Pages |
381–395 |
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Abstract |
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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ICDAR |
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DAG; 600.121; 600.140; 110.312 |
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Call Number |
Admin @ si @ RMG2021 |
Serial |
3572 |
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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 |
Abbreviated Journal |
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Volume |
12822 |
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Pages |
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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LNCS |
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ICDAR |
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DAG; 600.121; 600.140; 110.312 |
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Call Number |
Admin @ si @ MRG2021b |
Serial |
3571 |
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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part II |
Type |
Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
Abbreviated Journal |
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Volume |
12822 |
Issue |
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Pages |
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Keywords |
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Abstract |
This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.
The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding. |
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Address |
Lausanne, Switzerland, September 5-10, 2021 |
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Publisher |
Springer Cham |
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Editor |
Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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ISBN |
978-3-030-86330-2 |
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ICDAR |
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DAG |
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no |
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Call Number |
Admin @ si @ |
Serial |
3726 |
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Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Title |
Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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Book Chapter |
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Year |
2021 |
Publication |
Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 |
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Volume |
15 |
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Pages |
89–93 |
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Abstract |
Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces. |
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Springer Nature |
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Notes |
IAM; DAG; 600.120; 600.145; 600.139 |
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no |
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Call Number |
Admin @ si @ GRP2021 |
Serial |
3594 |
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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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Year |
2018 |
Publication |
Graphics Recognition. Current Trends and Evolutions |
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Volume |
11009 |
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Pages |
81-95 |
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Keywords |
Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
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Abstract |
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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Editor |
A. Fornes, B. Lamiroy |
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LNCS |
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978-3-030-02283-9 |
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Conference |
GREC |
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Notes |
DAG; 600.097; 601.302; 601.330; 600.121 |
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no |
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Call Number |
Admin @ si @ BRC2018 |
Serial |
3227 |
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Author |
Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke |
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Title |
Median Graph Computation by Means of Graph Embedding into Vector Spaces |
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Book Chapter |
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Year |
2013 |
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Graph Embedding for Pattern Analysis |
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Pages |
45-72 |
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In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant. |
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Springer New York |
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Editor |
Yun Fu; Yungian Ma |
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978-1-4614-4456-5 |
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DAG |
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no |
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Admin @ si @ FBV2013 |
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2421 |
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Author |
Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
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Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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E |
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Pages |
749-774 |
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Abstract |
The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-860-7 |
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Notes |
DAG; ADAS; 600.076; 600.077 |
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no |
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Call Number |
Admin @ si @ FoS2014 |
Serial |
2484 |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
Issue |
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Pages |
489-521 |
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Keywords |
Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation |
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Abstract |
This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Call Number |
Admin @ si @ LlR2014 |
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2380 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
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Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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523-551 |
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Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting |
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Abstract |
According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Call Number |
Admin @ si @ TaT2014 |
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2489 |
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