A new method for restoring high-resolution binary images is presented to improve legibility and OCR accuracy for low-resolution text images. The initially restored image is generated by simple techniques, and is then improved by integrating a variety of features obtained through image analysis. Missing strokes of characters are complemented based on topographic features. Contours of characters are then modified in terms of gradient magnitudes and curvatures along the contours. Finally, contours are beautified so that they look good to the human eye. The proposed method can deal with characters having complex structures such as Kanji, and entails relatively simple computation. Through experiments, it has been validated that the proposed method improves both OCR accuracy and legibility. In particular, smoothness and linearity along contours are significantly improved and strokes are restored correctly.
This paper describes an efficient algorithm for inverse halftoning of scanned color document images to resolve problems with interference patterns such as moire and graininess when the images are displayed or printed out. The algorithm is suitable for software implementation and useful for high quality printing or display of scanned document images delivered via networks from unknown scanners. A multi-resolution approach is used to achieve practical processing speed under software implementation. Through data-driven, adaptive, multi-scale processing, the algorithm can cope with a variety of input devices and requires no information on the halftoning method or properties (such as coefficients in dither matrices, filter coefficients of error diffusion kernels, screen angles, or dot frequencies). Effectiveness of the new algorithm is demonstrated through real examples of scanned color document images, as well as quantitative evaluations with synthetic data.
This paper describes a new approach to restoring scanned color document images where the backside image shows through the paper sheet. A new framework is presented for correcting show-through components using digital image processing techniques. First, the foreground components on the front side are separated from the background and backside components through locally adaptive binarization for each color component and edge magnitude thresholding. Background colors are estimated locally through color thresholding to generate a restored image, and then corrected adaptively through multi-scale analysis along with comparison of edge distributions between the original and the restored image. The proposed method does not require specific input devices or the backside to be input; it is able to correct unneeded image components through analysis of the front side image alone. Experimental results are given to verify effectiveness of the proposed method.
The factorization method is known to be robust and efficient for the recovery of shape and motion from an image sequence by applying Singular Value Decomposition to the tracking matrix. To get all-around 3-D data of an object, the all~around view of the object must be taken as pictures. This means that a long image sequence is required, and there is almost no feature point that can be tracked throughout all frames. This occurs because of occlusion. Consequently a large tracking matrix in which most elements are unknown is acquired. It is impractical to apply the conventional factorization method directly to such a tracking matrix, because most of the elements are unknown. Instead of applying the factorization method directly to the tracking matrix, the matrix is first divided into sub-matrices having overlapping portions. After unknown elements are estimated in each sub-matrix, the factorization method is applied to each sub-matrix to recover the partial 3-D data. Then the partial 3-D data is integrated into a whole according to the overlapped portions of each pair of sub-matrices. By modifying the factorization method in this split-and-merge manner, not only can the all-around 3-D data be recovered, but also the computation time is decreased dramatically.
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