Authors:
Dinh Cong Nguyen
1
;
Mathieu Delalandre
2
;
Donatello Conte
2
and
The Anh Pham
3
Affiliations:
1
Tours University, Tours City, France, Hong Duc University, Thanh Hoa and Vietnam
;
2
Tours University, Tours City and France
;
3
Hong Duc University, Thanh Hoa and Vietnam
Keyword(s):
Text Detection, LoG, Blobs, Key-points, Real-time, Estimators, DoG, Fast Gaussian Filtering, Scale-space, Stroke Model, Groundtruthing, Performance Characterization, Repeatability.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
This paper presents a state-of-the-art and a performance evaluation of real-time text detection methods, having particular focus on the family of Laplacian of Gaussian (LoG) operators with scale-invariance. The computational complexity of operators is discussed and an adaptation to text detection is obtained through the scale-space representation. In addition, a groundtruthing process and a characterization protocol are proposed, performance evaluation is driven with repeatability and processing time. The evaluation highlights a near-exact approximation with real-time operators at one to two orders of magnitude of execution time. The real-time operators are adapted to recent camera devices to process high resolution images. Perspectives are provided for operator robustness, optimization and characterization of the detection strategy.