Feature Extraction in Six Blocks to Detect and Recognize English Numbers

  • Heba Kh. Abbas Department of Physics, College of Science for women, University of Baghdad, Baghdad, Iraq
  • Haidar J. Mohamad Department of Physics, College of Science, Mustansiriyah University, Baghdad, Iraq https://orcid.org/0000-0003-2032-4080
Keywords: Fuzzy algorithm, isolated number, recognition, Normalized Absolute Error, feature extraction

Abstract

    The Fuzzy Logic method was implemented to detect and recognize English numbers in this paper. The extracted features within this method make the detection easy and accurate. These features depend on the crossing point of two vertical lines with one horizontal line to be used from the Fuzzy logic method, as shown by the Matlab code in this study. The font types are Times New Roman, Arial, Calabria, Arabic, and Andalus with different font sizes of 10, 16, 22, 28, 36, 42, 50 and 72. These numbers are isolated automatically with the designed algorithm, for which the code is also presented. The number’s image is tested with the Fuzzy algorithm depending on six-block properties only. Groups of regions (High, Medium, and Low) for each number showed unique behavior to recognize any number. Normalized Absolute Error (NAE) equation was used to evaluate the error percentage for the suggested algorithm. The lowest error was 0.001% compared with the real number. The data were checked by the support vector machine (SVM) algorithm to confirm the quality and the efficiency of the suggested method, where the matching was found to be 100% between the data of the suggested method and SVM. The six properties offer a new method to build a rule-based feature extraction technique in different applications and detect any text recognition with a low computational cost.

Published
2021-10-30
How to Cite
Abbas, H. K., & Mohamad, H. J. (2021). Feature Extraction in Six Blocks to Detect and Recognize English Numbers. Iraqi Journal of Science, 62(10), 3790-3803. https://doi.org/10.24996/ijs.2021.62.10.37
Section
Remote Sensing

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