Character energy and link energy-based text extraction in scene images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
GAS meter reading from real world images using a multi-net system
Pattern Recognition Letters
A text reading algorithm for natural images
Image and Vision Computing
Multi-script robust reading competition in ICDAR 2013
Proceedings of the 4th International Workshop on Multilingual OCR
Text extraction from natural scene image: A survey
Neurocomputing
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
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In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region candidates are generated through a modified K-means clustering algorithm, which references texture features, edge information and color information. The candidate labels are then verified in a global sense by the Markov Random Field model where collinearity weight is added as long as most texts are aligned. The proposed method achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database.