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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
MSER-based Real-Time Text Detection and Tracking |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3110 - 3115 |
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We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices. |
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Stockholm; August 2014 |
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1051-4651 |
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ICPR |
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DAG; 600.056; 601.158; 601.197; 600.077 |
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no |
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Admin @ si @ GoK2014a |
Serial |
2492 |
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Author |
David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes |
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Title |
Sequential Word Spotting in Historical Handwritten Documents |
Type |
Conference Article |
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Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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101 - 105 |
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In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a
sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered
index pages of family names. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 600.061; 600.056; 602.006; 600.077 |
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no |
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Admin @ si @ FML2014 |
Serial |
2462 |
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Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
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Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
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Journal |
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Year |
2014 |
Publication |
Manuscript Cultures |
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7 |
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Pages |
47-58 |
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Abstract |
With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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DAG; 600.061; 600.077 |
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no |
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Admin @ si @ |
Serial |
3190 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance |
Type |
Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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Pages |
3074 - 3079 |
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Keywords |
word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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Abstract |
Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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ICPR |
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Notes |
DAG; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ WEG2014a |
Serial |
2515 |
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Author |
Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre |
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Title |
A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts |
Type |
Conference Article |
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Year |
2014 |
Publication |
Digital Access to Textual Cultural Heritage Conference |
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Pages |
103-108 |
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In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts. |
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Madrid; May 2014 |
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978-1-4503-2588-2 |
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DATeCH |
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Notes |
DAG; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ FLM2014 |
Serial |
2516 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Novel Learning-free Word Spotting Approach Based on Graph Representation |
Type |
Conference Article |
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Year |
2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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Pages |
207-211 |
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Abstract |
Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAS |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ WEG2014b |
Serial |
2517 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité |
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Conference Article |
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Year |
2014 |
Publication |
Colloque International Francophone sur l'Écrit et le Document |
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Pages |
233-248 |
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Keywords |
word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example |
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Abstract |
Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Nancy; Francia; March 2014 |
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CIFED |
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Notes |
DAG; 600.061; 602.006; 600.077 |
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no |
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Call Number |
Admin @ si @ WEG2014c |
Serial |
2564 |
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Permanent link to this record |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
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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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489-521 |
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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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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ LlR2014 |
Serial |
2380 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
Abbreviated Journal |
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Volume |
D |
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Pages |
523-551 |
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Keywords |
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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no |
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Call Number |
Admin @ si @ TaT2014 |
Serial |
2489 |
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Permanent link to this record |
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Author |
Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal |
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Title |
Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing |
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Journal Article |
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Year |
2014 |
Publication |
Multimedia Tools and Applications |
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MTAP |
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72 |
Issue |
1 |
Pages |
515-539 |
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In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. |
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Springer US |
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1380-7501 |
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Notes |
DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ SDT2014 |
Serial |
2357 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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7-11 |
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Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel |
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Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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LNCS |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ DLB2014 |
Serial |
2698 |
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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
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Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
Abbreviated Journal |
IR |
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Volume |
17 |
Issue |
5-6 |
Pages |
545-562 |
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Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Abstract |
Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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1386-4564 |
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DAG; 600.077 |
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no |
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Admin @ si @ RHR2013 |
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2342 |
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Author |
T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger |
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Title |
The achromatic locus: Effect of navigation direction in color space |
Type |
Journal Article |
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Year |
2014 |
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Journal of Vision |
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VSS |
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14 (1) |
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25 |
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1-11 |
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achromatic; unique hues; color constancy; luminance; color space |
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5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes. |
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DAG; 600.077 |
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Admin @ si @ CPX2014 |
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2418 |
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Author |
A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
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2014 |
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Handbook of Document Image Processing and Recognition |
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D |
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591-646 |
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Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
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The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
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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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Admin @ si @ KeK2014 |
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2425 |
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Author |
Anjan Dutta |
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Inexact Subgraph Matching Applied to Symbol Spotting in Graphical Documents |
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2014 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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There is a resurgence in the use of structural approaches in the usual object recognition and retrieval problem. Graph theory, in particular, graph matching plays a relevant role in that. Specifically, the detection of an object (or a part of that) in an image in terms of structural features can be formulated as a subgraph matching. Subgraph matching is a challenging task. Specially due to the presence of outliers most of the graph matching algorithms do not perform well in subgraph matching scenario. Also exact subgraph isomorphism has proven to be an NP-complete problem. So naturally, in graph matching community, there are lot of efforts addressing the problem of subgraph matching within suboptimal bound. Most of them work with approximate algorithms that try to get an inexact solution in estimated way. In addition, usual recognition must cope with distortion. Inexact graph matching consists in finding the best isomorphism under a similarity measure. Theoretically this thesis proposes algorithms for solving subgraph matching in an approximate and inexact way.
We consider the symbol spotting problem on graphical documents or line drawings from application point of view. This is a well known problem in the graphics recognition community. It can be further applied for indexing and classification of documents based on their contents. The structural nature of this kind of documents easily motivates one for giving a graph based representation. So the symbol spotting problem on graphical documents can be considered as a subgraph matching problem. The main challenges in this application domain is the noise and distortions that might come during the usage, digitalization and raster to vector conversion of those documents. Apart from that computer vision nowadays is not any more confined within a limited number of images. So dealing a huge number of images with graph based method is a further challenge.
In this thesis, on one hand, we have worked on efficient and robust graph representation to cope with the noise and distortions coming from documents. On the other hand, we have worked on different graph based methods and framework to solve the subgraph matching problem in a better approximated way, which can also deal with considerable number of images. Firstly, we propose a symbol spotting method by hashing serialized subgraphs. Graph serialization allows to create factorized substructures such as graph paths, which can be organized in hash tables depending on the structural similarities of the serialized subgraphs. The involvement of hashing techniques helps to reduce the search space substantially and speeds up the spotting procedure. Secondly, we introduce contextual similarities based on the walk based propagation on tensor product graph. These contextual similarities involve higher order information and more reliable than pairwise similarities. We use these higher order similarities to formulate subgraph matching as a node and edge selection problem in the tensor product graph. Thirdly, we propose near convex grouping to form near convex region adjacency graph which eliminates the limitations of traditional region adjacency graph representation for graphic recognition. Fourthly, we propose a hierarchical graph representation by simplifying/correcting the structural errors to create a hierarchical graph of the base graph. Later these hierarchical graph structures are matched with some graph matching methods. Apart from that, in this thesis we have provided an overall experimental comparison of all the methods and some of the state-of-the-art methods. Furthermore, some dataset models have also been proposed. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Umapada Pal |
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978-84-940902-4-0 |
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DAG; 600.077 |
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Admin @ si @ Dut2014 |
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2465 |
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