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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part IV |
Type |
Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
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Volume |
12824 |
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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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Editor |
Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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978-3-030-86336-4 |
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ICDAR |
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DAG |
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no |
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Call Number  |
Admin @ si @ |
Serial |
3728 |
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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part I |
Type |
Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
Abbreviated Journal |
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Volume |
12821 |
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Pages |
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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: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing (NLP) for document understanding, and graphics, diagram and math recognition. |
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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 |
978-3-030-86548-1 |
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Conference |
ICDAR |
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Notes |
DAG |
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no |
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Call Number |
Admin @ si @ |
Serial |
3725 |
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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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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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Conference |
ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3726 |
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Author |
Josep Llados |
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Title |
The 5G of Document Intelligence |
Type |
Conference Article |
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Year |
2021 |
Publication |
3rd Workshop on Future of Document Analysis and Recognition |
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Address |
Lausanne; Suissa; September 2021 |
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Conference |
FDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3677 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi |
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Title |
Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
7887 |
Issue |
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Pages |
133-140 |
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Keywords |
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Abstract |
In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. |
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Address |
Madeira; Portugal; June 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
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Conference |
IbPRIA |
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Notes |
DAG; 605.203 |
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no |
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Call Number |
Admin @ si @ ACS2013 |
Serial |
2328 |
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Author |
Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
Type |
Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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Volume |
150 |
Issue |
A |
Pages |
147-154 |
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Keywords |
document image analysis; stochastic context-free grammars; text classi cation features |
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Abstract |
In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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Notes |
DAG; 601.158; 600.077; 600.061 |
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no |
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Call Number |
Admin @ si @ ACS2015 |
Serial |
2531 |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A Non-Rigid Feature Extraction Method for Shape Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
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Pages |
987-991 |
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Abstract |
This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost. |
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Address |
Beijing; China; September 2011 |
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ISBN |
978-0-7695-4520-2 |
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ICDAR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ AFV2011 |
Serial |
1763 |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A Deformable HOG-based Shape Descriptor |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Issue |
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Pages |
1022-1026 |
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Abstract |
In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval |
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Address |
Washington; USA; August 2013 |
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ISSN |
1520-5363 |
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ICDAR |
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DAG |
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Call Number |
Admin @ si @ AFV2013 |
Serial |
2326 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Handwritten Word Spotting with Corrected Attributes |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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Pages |
1017-1024 |
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Abstract |
We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. |
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Sydney; Australia; December 2013 |
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ISSN |
1550-5499 |
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Conference |
ICCV |
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DAG |
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Call Number |
Admin @ si @ AGF2013 |
Serial |
2327 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Word Spotting and Recognition with Embedded Attributes |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
36 |
Issue |
12 |
Pages |
2552 - 2566 |
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Abstract |
This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. |
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ISSN |
0162-8828 |
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Notes |
DAG; 600.056; 600.045; 600.061; 602.006; 600.077 |
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Call Number |
Admin @ si @ AGF2014a |
Serial |
2483 |
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