A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Recognition of Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Binarization Methods for Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Document Image Binarization Based on Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
Digital Image Processing
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive degraded document image binarization
Pattern Recognition
A New Approach for Skew Correction of Documents Based on Particle Swarm Optimization
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
Unsupervised range-constrained thresholding
Pattern Recognition Letters
Shape based local thresholding for binarization of document images
Pattern Recognition Letters
Motion detection with pyramid structure of background model for intelligent surveillance systems
Engineering Applications of Artificial Intelligence
Image bilevel thresholding based on stable transition region set
Digital Signal Processing
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
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Document image binarization involves converting gray level images into binary images, which is a feature that has significantly impacted many portable devices in recent years, including PDAs and mobile camera phones. Given the limited memory space and the computational power of portable devices, reducing the computational complexity of an embedded system is of priority concern. This work presents an efficient document image binarization algorithm with low computational complexity and high performance. Integrating the advantages of global and local methods allows the proposed algorithm to divide the document image into several regions. A threshold surface is then constructed based on the diversity and the intensity of each region to derive the binary image. Experimental results demonstrate the effectiveness of the proposed method in providing a promising binarization outcome and low computational cost.