Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Automatic fuzzy rule base generation for on-line handwritten alphanumeric character recognition
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
Document cleanup using page frame detection
International Journal on Document Analysis and Recognition
Efficient impulse noise reduction via local directional gradients and fuzzy logic
Fuzzy Sets and Systems
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Clutter Noise Removal in Binary Document Images
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Gabor filters-based feature extraction for character recognition
Pattern Recognition
Image denoising in contourlet domain based on a normal inverse Gaussian prior
Digital Signal Processing
Two-step fuzzy logic-based method for impulse noise detection in colour images
Pattern Recognition Letters
Decision-based fuzzy image restoration for noise reduction based on evidence theory
Expert Systems with Applications: An International Journal
A fuzzy filter for the removal of random impulse noise in image sequences
Image and Vision Computing
Additive noise removal using a novel fuzzy-based filter
Computers and Electrical Engineering
Stroke-Like Pattern Noise Removal in Binary Document Images
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Hi-index | 0.01 |
The removal of noise patterns in handwritten images requires careful processing. A noise pattern belongs to a class that we have either seen or not seen before. In the former case, the difficulty lies in the fact that some types of noise patterns look similar to certain characters or parts of characters. In the latter case, we do not know the class of noise in advance which excludes the possibility of using parametric learning methods. In order to address these difficulties, we formulate the noise removal and recognition as a single optimization problem, which can be solved by expectation maximization given that we have a recognition engine that is trained for clean images. We show that the processing time for a noisy input is higher than that of a clean input by a factor of two times the number of connected components of the input image in each iteration of the optimization process. Therefore, in order to speed up the convergence, we propose to use fuzzy inference systems in the initialization step of the optimization process. Fuzzy inference systems are based on linguistic rules that facilitate the definition of some common classes of noise patterns in handwritten images such as impulsive noise and background lines. We analyze the performance of our approach both in terms of recognition rate and speed. Our experimental results on a database of real-world handwritten images corroborate the effectiveness and feasibility of our approach in removing noise patterns and thus improving the recognition performance for noisy images.