TSD-CPI: Traffic Sign Detection Technique Based on Centroid Position Identification in Text Mining
R. Karthika1, S. Murugan2
1R. Karthika, Research Scholar, Department of Computer Science, Memorial College (Autonomous), Puthanampatti, (Tamil Nadu) India.
2S. Murugan, Associate Professor, Department of Computer Science, Memorial College (Autonomous), Puthanampatti, (Tamil Nadu) India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1649-1653 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3056129219/2019©BEIESP | DOI: 10.35940/ijeat.B3056.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Detecting and Identifying traffic sign is a complicated issue due to the changing variability in cloud conditions. Hence, it is necessary to identify and detect of traffic signs during journey. The traffic text sign identification fails due to noise, blur, distortion and occlusion. In order to identify the text, a technique should be adapted that recognizes the text with improved accuracy. In existing algorithms such as Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) were not detecting the Centroid position. In this paper, the text Centroid of position sign is detected using text color, font and size. During journey, if the text is blurred, this Traffic Sign Detection Technique based on Centroid Position Identification (TSD-CPI) K-means algorithm for clustering is possible to use. As a result, it detects the text that with improved accuracy. Ultimately, it reduces the processing time. The experimental result reveals that using WEKA-3.8 with the proposed technique shows improvement over the existing algorithms in terms of precision and Recall which enhance the accuracy in text mining.
Keywords: Histogram, Gradients, Support Vector Machine, Centroid and K-Means.