Digital Image Processing
Text/Graphics Separation in Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Text/Graphic labelling of Ancient Printed Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Text Segmentation from Complex Background Using Sparse Representations
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Toponym Recognition in Scanned Color Topographic Maps
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Segmentation of Text and Graphics from Document Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Types of arcs in a fuzzy graph
Information Sciences: an International Journal
Node connectivity and arc connectivity of a fuzzy graph
Information Sciences: an International Journal
Grouping using factor graphs: an approach for finding text with a camera phone
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Fuzzy Graph-Theoretical Clustering Approach on Spatial Relationship Constrain
ISIE '11 Proceedings of the 2011 International Conference on Intelligence Science and Information Engineering
An MRF Model for Binarization of Natural Scene Text
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
FoSA: F* Seed-growing Approach for crack-line detection from pavement images
Image and Vision Computing
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
Map image text segmentation has always been one of the difficult tasks because of its variety. The texts in a map may have the myriad background consists of various intensity values, different orientations, overlapping objects, intersected lines etc. Common problems for text extraction from map images are the lack of prior knowledge of text features such as color, font, size and orientation as well as the location of the probable text regions. Extracted texts can be used as an input to OCR for recognition. This paper presents an approach for text segmentation from map images using fuzzy graph analysis. Fuzzy graph is constructed from the map image. Fuzzy similarity value between two nodes within text region will be higher than other non-text regions. Seed points are selected through the fuzzy graph analysis. These seed points lie within texts in a map image. F* seed growing algorithm is used here for text localization. The originality of this work lies in the fuzzy graph construction from map image and selection of seed points. The proposed text segmentation approach is tested on a collected dataset of paper map images (containing texts in Indian languages; like Bangla, Hindi etc.) and the results are encouraging.