A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Text/Graphics Separation in Maps
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Text/Graphics Separation Revisited
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Graphics Recognition - from Re-engineering to Retrieval
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Word and Symbol Spotting Using Spatial Organization of Local Descriptors
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Multi-Oriented and Multi-Sized Touching Character Segmentation Using Dynamic Programming
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A new text detection algorithm for content-oriented line drawing image retrieval
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult. Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.