We present a formal model for processing gray-scale images of business forms such as bank cheques. The formal model is based on a new hybrid-based approach namely the base lines. In fact, to segment handwritten and ha...
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We present a formal model for processing gray-scale images of business forms such as bank cheques. The formal model is based on a new hybrid-based approach namely the base lines. In fact, to segment handwritten and hand-printed data from bank cheques, knowledge rules and base lines will have important roles to segment and extract the information from bank cheques. The architectural design as well as the major components of the system is discussed in full detail. Moreover, the significant use of the morphological followed by the topological processing on gray-scale images is used as a major aspect to restore the lost information after the elimination of the background and the base lines from the gray-scale cheques.
Based on wavelets, a new theoretical method has been developed to process form documents. In this method, two-dimensional multiresolution analysis (MSA), wavelet decomposition algorithm, and compactly supported orthon...
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Based on wavelets, a new theoretical method has been developed to process form documents. In this method, two-dimensional multiresolution analysis (MSA), wavelet decomposition algorithm, and compactly supported orthonormal wavelets are used to transform a document image into sub-images. According to these sub-images, the reference lines of forms can be extracted, and knowledge about the geometric structure of the document can be acquired. Experiments prove that this new method can be applied to process documents with promising results.
Explores the possibility of using a neural-net approach to the task of combining multiple classifiers. A combination principle is proposed, and a novel combination technique, called an associative switch, is developed...
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Explores the possibility of using a neural-net approach to the task of combining multiple classifiers. A combination principle is proposed, and a novel combination technique, called an associative switch, is developed for solving the problem. The switch is controlled by a neural net trained by the backpropagation technique with a modified energy criterion. When an unlabeled pattern is the input to each individual classifier, it also goes to the neural net for associatively calling out a code which controls the switch to decide whether the result of each classifier could pass through as a final result. This associative switch is applied to a problem of combining multiple classifiers for recognizing totally unconstrained handwritten numerals.< >
The authors studied several computer algorithms by evaluating the recognition rates on distinct parts of handprinted characters. A regional decomposition method is to facilitate pattern analysis and recognition, by wh...
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The authors studied several computer algorithms by evaluating the recognition rates on distinct parts of handprinted characters. A regional decomposition method is to facilitate pattern analysis and recognition, by which a complicated pattern can be split into simpler parts or subpatterns. A hierarchy model is also proposed to identify the attributes in different hierarchic levels of patterns so that the recognition rates of the different parts can be calculated by a simplified, uniform, computational scheme. The computed results coincide with those of subjective experiments, but they are more precise and complete. The total mean score of recognition rates of parts obtained from the proposed algorithms was 30% higher than that from human experiments.< >
A novel version of the subspace patternrecognition method, called the dual subspace patternrecognition (DSPR) method, is proposed, and a neural-network model with a combination of modified Hebbian and anti-Hebbian l...
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A novel version of the subspace patternrecognition method, called the dual subspace patternrecognition (DSPR) method, is proposed, and a neural-network model with a combination of modified Hebbian and anti-Hebbian learning rules is developed for implementing the DSPR method. An experimental comparison was made on an example data set by using this model and a three-layer forward net with back propagation learning. The results demonstrate that this model can outperform the back propagation model in some applications.< >
An entropy-reduced transformation (ERT) approach to nonlinear shape restoration has been developed. Nonlinear shape distortions are formulated using nonlinear shape transformations derived from the finite-element theo...
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An entropy-reduced transformation (ERT) approach to nonlinear shape restoration has been developed. Nonlinear shape distortions are formulated using nonlinear shape transformations derived from the finite-element theory. Several algorithms which perform the nonlinear shape transformations are given. The inverse nonlinear shape transformation algorithms are described. Some application experiments are described, and results are given.< >
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