Extraction of Character Regions through Machine Learning and Filtering
Seok-Woo Jang1, Sang-Hong Lee2
1Seok-Woo Jang, Department of Software, Anyang University, Anyang, South Korea.
2Sang-Hong Lee, Department of Computer Engineering, Anyang University, Anyang, South Korea.
Manuscript received on 18 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 16 September 2019 | PP: 311-315 | Volume-8 Issue-2S6 July 2019 | Retrieval Number: B10590782S619/2019©BEIESP | DOI: 10.35940/ijrte.B1059.0782S619
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© 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: Characters in images are able to provide main information of the image. Therefore, it is important to analyze various kinds of image data and accurately extract the characters in images. This study proposes a new method of excluding background regions and accurately detecting character regions from input images with the uses of MCT features and Adaboost algorithm. The proposed method first extracts candidate character regions from input images with the uses of MCT features and Adaboost algorithm. It then excludes non-character regions and detects real character regions from the extracted candidate regions with the use of geometrical features. In the experiment of this study, the proposed method more robustly detected character regions from various input color images than a conventional method. For performance comparison, this study compared the method based on existing texture analysis and the proposed method. In this study, to qualitatively evaluate the performance of the proposed method of extracting license plate regions, the accuracy measure was defined. The measure is used to show the ratio of the accurately extracted character regions to all character regions of an image. The conventional method using the frequency factor-based texture information had many errors of character region detection, since it failed to execute binarization of background and character regions properly. On contrary, the proposed method made use of MCT features and Adaboost algorithm, effectively filtered candidate regions with the use of geometrical features, so that it detected character regions more accurately. The proposed character detection method is expected to be usefully applied to the fields of pattern recognition and image processing, such as store sign recognition and license plate recognition.
Keywords: Filtering, Machine Learning, Character Data, Feature Acquisition, Candidate Region.
Scope of the Article: Machine Learning