A novel statistical feature extraction method for textual images: Optical font recognition

Abstract

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.