Optical Font Recognition Using Typographical Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Font Recognition Based on Global Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
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DIA '97 Proceedings of the 1997 Workshop on Document Image Analysis
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Linguistic Optical Font Recognition Using Stroke Templates
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Character Independent Font Recognition on a Single Chinese Character
IEEE Transactions on Pattern Analysis and Machine Intelligence
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International Journal on Document Analysis and Recognition
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Pattern Recognition
International Journal on Document Analysis and Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
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ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
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ACT '09 Proceedings of the 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
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ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
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UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Pattern Recognition Letters
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The binary image is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. In this regard, pattern recognition studies involve document analysis techniques. Optical font recognition is among the pattern recognition techniques that are becoming popular today. In this paper, we propose an enhanced global feature extraction method based on the on statistical analysis of the behavior of edge pixels in binary images. A novel method in feature extraction for binary images has been proposed whereby the behavior of the edge pixels between a white background and a black pattern in a binary image captures information about the properties of the pattern. The proposed method is tested on an Arabic calligraphic script image for an optical font recognition application. To evaluate the performance of our proposed method, we compared it with a gray-level co occurrence matrix (GLCM). We classified the features using a multilayer artificial immune system, a Bayesian network, decision table rules, a decision tree, and a multilayer network to identify which approach is most suitable for our proposed method. The results of the experiments show that the proposed method with a decision tree classifier can boost the overall performance of optical font recognition.