An introduction to digital image processing
An introduction to digital image processing
Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Edge and Corner Detection by Photometric Quasi-Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Text segmentation in color images using tensor voting
Image and Vision Computing
Colour text segmentation in web images based on human perception
Image and Vision Computing
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
A Novel Algorithm for Text Detection and Localization in Natural Scene Images
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
H-DIBCO 2010 - Handwritten Document Image Binarization Competition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
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In this paper, we propose a novel method for detecting and segmenting text layers in complex images. This method is robust against degradations such as shadows, non-uniform illumination, low-contrast, large signaldependent noise, smear and strain. The proposed method first uses a geodesic transform based on a morphological reconstruction technique to remove dark/light structures connected to the borders of the image and to emphasize on objects in center of the image. Next uses a method based on difference of gamma functions approximated by the Generalized Extreme Value Distribution (GEVD) to find a correct threshold for binarization. The main function of this GEVD is to find the optimum threshold value for image binarization relatively to a significance level. The significance levels are defined in function of the background complexity. In this paper, we show that this method is much simpler than other methods for text binarization and produces better text extraction results on degraded documents and natural scene images.