An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
A Video Based Interface to Textual Information for the Visually Impaired
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Progress in Camera-Based Document Image Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph Cut Algorithm for Generalized Image Deconvolution
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Binarization of Badly Illuminated Document Images through Shading Estimation and Compensation
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Document Image Binarisation Using Markov Field Model
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Feature Based Binarization of Document Images Degraded by Uneven Light Condition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A multi-scale framework for adaptive binarization of degraded document images
Pattern Recognition
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In this paper, a novel Markov random fields (MRF) based binarization algorithm is proposed to segment foreground text from document images captured using hand-held devices (such as cell-phone or digital camera). In the MRF based framework, an edge potential feature is extracted to preserve the strokes of foreground text and to remove isolated noise and an intensity feature is used to smooth the entire document image. Prior to binarization, we use a nonlinear function to enhance the quality of document images which suffer from insufficient or uneven illumination. Experimental results show that our method outperforms other state-of-the-art approaches.