KEYWORDS: Distortion, Digital cameras, Image processing, Optical character recognition, Cameras, 3D modeling, Process modeling, Error analysis, Image analysis, 3D image processing
Distortion correction methods for digital camera document images of thick volumes or curved papers become important
for camera-based document recognition technologies. In this paper we propose a novel distortion correction method for
digital camera document images based on "shape from parallel geodesics." This method considers the following features:
parallel lines corresponding to character strings or ruled lines of tables on extended surface become parallel geodesics on
a curved paper surface and a smoothly curved paper can be modeled by a ruled surface, which is sweep surface of rulings.
The projected geodesics and rulings exist in the input image derived from perspective transformation. The presented
method extracts the projected geodesics, estimates the projected rulings in the input image, estimates the ruled surface
that models the curved paper, and generates the corrected image, in this order. The projected rulings are estimated by the
condition derived from only parallelism of geodesics without the requirements for equal spacing. This method can
estimate the ruled surface model directly by numerical operations of differentiation, integration and matrix inversion
without any iterative calculation. We also report on experiments that show the effectiveness of the proposed method.
To recognize a handwritten check mark on a pre-printed character with OCR, we need to separate the pre-print and the superimposed check mark. By the previous method, an unmarked form image and marked one are matched and overlapping part is removed. Then the remaining pattern is regarded as a superimposed mark pattern. Therefore when the mark pattern and the pre-print is overlapped, the overlapping part is removed with the pre-print. It sometimes causes a decision error of the existence of the check mark. In this paper, we propose a new method to separate a superimposed pattern to preprints using directional decomposition of an image for precise recognition of the check mark.
Projection analysis methods have been widely used to segment Japanese character strings. However, if adjacent characters have overhanging strokes or a touching point doesn't correspond to the histogram minimum, the methods are prone to result in errors. In contrast, non-projection analysis methods being proposed for use on numerals or alphabet characters cannot be simply applied for Japanese characters because of the differences in the structure of the characters. Based on the oversegmenting strategy, a new pre-segmentation method is presented in this paper: touching patterns are represented as graphs and touching strokes are regarded as the elements of proper edge cutsets. By using the graph theoretical technique, the cutset martrix is calculated. Then, by applying pruning rules, potential touching strokes are determined and the patterns are over segmented. Moreover, this algorithm was confirmed to be valid for touching patterns with overhanging strokes and doubly connected patterns in simulations.
One of the critical problems of an off-line handwritten character reader system is determining which patterns to read and which to ignore, as a form or a document contains not only characters but also spots and deletions. As long as they don't fit conditions for rejection, they cause recognition errors. Particularly, patterns of deleted single-character are difficult to be distinguished from a character, because their sizes are almost the same as that of a character and their shapes have variety. In this article, we proposed a method to detect such deletions in handwritten digits using topological and geometrical image- features suitable for detecting them; Eular number, pixel density, number of endpoint, maximum crossing counts and number of peaks of histogram. For precise detection, thresholds of the image features are adaptively selected according to their recognition results.
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