A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Methodology for Gray-Scale Character Segmentation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of touching characters using an MLP
Pattern Recognition Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Extraction of Topographic Features for Gray Scale Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining SVM Classifiers for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Character Segmentation-by-Recognition Using Log-Gabor Filters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Text region extraction algorithm on steel making process
ROCOM'08 Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology
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There are many algorithms for character segmentation [1]. Many people have been making researches in segmentation of touching or overlapping character up to now, but most of algorithms cannot apply to the text region of slab management numbers marked on the slab in the steel image. Because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in all cases This paper proposes a new algorithm for character segmentation using combined profile analysis and recognition based method. Besides the proposed algorithm converts gray image to binary image using method of adjusting brightness and contrast in pre-processing step. The experimental results show high recognition rates of slab management numbers marked on the slab about various text region images.