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Label Restoration Using Biquadratic Transformation
Le, Huy Phat,Nguyen, Toan Dinh,Lee, Guee-Sang The Korea Contents Association 2010 International Journal of Contents Vol.6 No.1
Recently, there has been research to use portable digital camera to recognize objects in natural scene images, including labels or marks on a cylindrical surface. In many cases, text or logo in a label can be distorted by a structural movement of the object on which the label resides. Since the distortion in the label can degrade the performance of object recognition, the label should be rectified or restored from deformations. In this paper, a new method for label detection and restoration in digital images is presented. In the detection phase, the Hough transform is employed to detect two vertical boundaries of the label, and a horizontal edge profile is analyzed to detect upper-side and lower-side boundaries of the label. Then, the biquadratic transformation is used to restore the rectangular shape of the label. The proposed algorithm performs restoration of 3D objects in a 2D space, and it requires neither an auxiliary hardware such as 3D camera to construct 3D models nor a multi-camera to capture objects in different views. Experimental results demonstrate the effectiveness of the proposed method.
Rectification of Perspective Text Images on Rectangular Planes
Le, Huy Phat,Madhubalan, Kavitha,Lee, Guee-Sang The Korea Contents Association 2010 International Journal of Contents Vol.6 No.4
Natural images often contain useful information about the scene such as text or company logos placed on a rectangular shaped plane. The 2D images captured from such objects by a camera are often distorted, because of the effects of the perspective projection camera model. This distortion makes the acquisition of the text information difficult. In this study, we detect the rectangular object on which the text is written, then the image is restored by removing the perspective distortion. The Hough transform is used to detect the boundary lines of the rectangular object and a bilinear transformation is applied to restore the original image.
Combining Fuzzy C-means Clustering and Flood Filling Algorithm for Enhancing Text Binarization
Huy Phat Le,Toan Dinh Nguyen,Jonghyun Park,GuessSang Lee 한국멀티미디어학회 2009 한국멀티미디어학회 학술발표논문집 Vol.2009 No.1
Text binarization is an important step in text understanding due to the fact that ORC (Optical Character Recognition) system only understands the binarized image. The more accurate the binarized text is; the better result the ORC system works. In this paper, a novel binarization method is proposed to binarize text from complex color images. First, the Fuzzy C-means algorithm is used to group similar pixels color in an image. The flood filling algorithm is then used to remove noise components in the background. Finally, we used Otsu global binarization method to binarize the text image. The experimental results show that our method outperforms the Otsu global binarization method.