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Author |
Josep Llados; Daniel Lopresti; Seiichi Uchida (eds) |
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Title |
16th International Conference, 2021, Proceedings, Part I |
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Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
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12821 |
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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The papers are organized into the following topical sections: historical document analysis, document analysis systems, handwriting recognition, scene text detection and recognition, document image processing, natural language processing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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Springer Cham |
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Josep Llados; Daniel Lopresti; Seiichi Uchida |
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LNCS |
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978-3-030-86548-1 |
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ICDAR |
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DAG |
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no |
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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 |
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Book Whole |
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Year |
2021 |
Publication |
Document Analysis and Recognition – ICDAR 2021 |
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Volume |
12822 |
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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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978-3-030-86330-2 |
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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 |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Towards Modelling an Attention-Based Text Localization Process |
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Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
7887 |
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Pages |
296-303 |
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Keywords |
text localization; visual attention; eye guidance |
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Abstract |
This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-38627-5 |
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IbPRIA |
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DAG |
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no |
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Call Number |
Admin @ si @ CKL2013 |
Serial |
2291 |
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Author |
Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin |
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Title |
Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval |
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Journal Article |
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Year |
2012 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
35 |
Issue |
12 |
Pages |
2916-2929 |
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Abstract |
This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset. |
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0162-8828 |
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978-1-4577-0394-2 |
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DAG |
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no |
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Admin @ si @ GLG 2012b |
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2008 |
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Author |
Anjan Dutta; Umapada Pal; Josep Llados |
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Title |
Compact Correlated Features for Writer Independent Signature Verification |
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Conference Article |
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Year |
2016 |
Publication |
23rd International Conference on Pattern Recognition |
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This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300. |
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Cancun; Mexico; December 2016 |
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ICPR |
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Notes |
DAG; 600.097 |
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no |
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Call Number |
Admin @ si @ DPL2016 |
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2875 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1078-1082 |
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This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets. |
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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; 600.056; 600.061; 601.152 |
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Call Number |
Admin @ si @ DLB2013a |
Serial |
2358 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Document Seal Detection Using Ght and Character Proximity Graphs |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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44 |
Issue |
6 |
Pages |
1282-1295 |
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Keywords |
Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition |
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This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes. |
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Elsevier |
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DAG |
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Admin @ si @ RPL2011 |
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1820 |
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Author |
Suman Ghosh; Lluis Gomez; Dimosthenis Karatzas; Ernest Valveny |
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Title |
Efficient indexing for Query By String text retrieval |
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Conference Article |
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2015 |
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6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1236 - 1240 |
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This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings. |
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Nancy; France; August 2015 |
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CBDAR |
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DAG; 600.077 |
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Admin @ si @ GGK2015 |
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2693 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |
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Title |
A string-based method to recognize symbols and structural textures in architectural plans |
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Conference Article |
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Year |
1997 |
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2nd IAPR Workshop on Graphics Recognition |
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91-103 |
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This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Nancy, France |
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DAG; IAM |
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IAM @ iam @ SLE1997 |
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1498 |
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Author |
Josep Llados; Gemma Sanchez; Enric Marti |
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A string based method to recognize symbols and structural textures in architectural plans |
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Book Chapter |
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1998 |
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Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers |
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LNCS |
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1389 |
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1998 |
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91-103 |
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This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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IAM @ iam @ SLE1998 |
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1573 |
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