| |
Records |
Links |
|
Author |
Lluis Gomez; Dimosthenis Karatzas |
|
| |
Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
19 |
Issue |
4 |
Pages |
335-349 |
|
| |
Keywords  |
scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
|
| |
Abstract |
Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.056; 601.197 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GoK2016a |
Serial |
2862 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
|
| |
Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
80 |
Issue |
|
Pages |
64-82 |
|
| |
Keywords |
Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
|
| |
Abstract |
Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.097; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RSJ2018 |
Serial |
3096 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Josep Llados; Horst Bunke; Enric Marti |
|
| |
Title |
Finding rotational symmetries by cyclic string matching |
Type |
Journal Article |
| |
Year |
1997 |
Publication |
Pattern recognition letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
18 |
Issue |
14 |
Pages |
1435-1442 |
|
| |
Keywords |
Rotational symmetry; Reflectional symmetry; String matching |
|
| |
Abstract |
Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG;IAM; |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ LBM1997a |
Serial |
1562 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; Josep Llados |
|
| |
Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
|
| |
Volume |
12 |
Issue |
2 |
Pages |
83-96 |
|
| |
Keywords |
Performance evaluation; Symbol Spotting; Graphics Recognition |
|
| |
Abstract |
Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1433-2833 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
DAG @ dag @ RuL2009a |
Serial |
1166 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
|
| |
Title |
Fuzzy Multilevel Graph Embedding |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
46 |
Issue |
2 |
Pages |
551-565 |
|
| |
Keywords |
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
|
| |
Abstract |
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0031-3203 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
DAG; 600.042; 600.045; 605.203 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LRL2013a |
Serial |
2270 |
|
| Permanent link to this record |
