Symbol Spotting in Electronic Images Using Morphological Filters and Hough Transform - Ramadhan, Dilovan S.; Al-Khaffaf, Hasan S. M. (Science Journal of University of Zakho, 2022)
In this paper, two algorithms (a preliminary and enhanced algorithm) to detect electronic symbols in document images are proposed. Morphological operations coupled with Hough transform are used in the proposed methodology. The objective of the proposed algorithms is to detect electronic symbols of open and closed shapes. The methods can successfully spot many complex types of electronic symbols such as Fixed Resistor; Zener Diode; Pn Junction Diode; NPN transistor; Not Gate; Input and Output Terminals; Ground; Single Cell Battery; Transformer; and LED symbols. The experimental results on the Systems Evaluation Synthetic Documents (SESYD) dataset show that the proposed preliminary method detects 86.2%, (12151 symbols out of 14100 symbols), precision of 0.94, recall of 0.91, and F-measure of 0.92. An enhanced algorithm that used line Hough transform is also demonstrated with accuracy of 91.2% (12864 symbols out of 14100 symbols), precision of 0.97, recall of 0.93, and F-measure of 0.95).