Hirotomo Aso

A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Dynamical characteristics of linear cellular automata

Abstract Dynamical characteristics of linear cellular automata are discussed algebraically, whose cell space and state space are an Abelian group and a finite commutative ring, respectively, instead of a lattice space and a residue class. One of the main results is a characterization of the dynamical structures with relation to what the unit configuration is. It is also shown that a linear cellular automaton with the state space of a residue class of an integer m can be decomposed in parallel into automata with the one of a power of a prime which is a factor of m . Using those results, the proofs of known results are improved concerning C -surjectivity, C -injectivity, and finite-order property for linear cellular automata and presented in a unified manner.

Extracting curved text lines using local linearity of the text line

Abstract. In order to enhance the ability of document analysis systems, we need a text line extraction method which can handle not only straight text lines but also text lines in various shapes. This paper proposes a new method called Extended Linear Segment Linking (ELSL for short), which is able to extract text lines in arbitrary orientations and curved text lines. We also consider the existence of both horizontally and vertically printed text lines on the same page. The new method can produce text line candidates for multiple orientations. We verify the ability of the method by some experiments as well.

A fast algorithm for a k‐NN classifier based on the branch and bound method and computational quantity estimation

The nearest neighbor rule or k-nearest neighbor rule is a technique of nonparametric pattern recognition. Its algorithm is simple and the error is smaller than twice the Bayes error if there are enough training samples. However, it requires an enormous amount of computation, proportional to the number of samples and the number of dimensions of the feature vector. In this paper, a fast algorithm for the k-nearest neighbor rule based on the branch and bound method is proposed. Moreover, a new training algorithm for constructing a search tree that can reduce the computational quantity is proposed. Experimental results show the effectiveness of the proposed algorithms.

A noise-adaptive discriminant function and its application to blurred machine-printed Kanji recognition

Accurate recognition of blurred images is a practical but previously mostly overlooked problem. In the paper, we quantify the level of noise in blurred images and propose a modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

Tooth shape reconstruction from ct images using spline Curves

It is desired to obtain three-dimensional shapes of teeth without extracting them physically in order to construct teeth database. In this paper, we propose a method for reconstructing three-dimensional shape of a tooth from the images acquired by a dental micro CT. First, initial contour of a tooth is obtained from an image in which the tooth is not buried in the bone and does not touch another tooth. A contour is determined in the adjacent image by searching the positions where the edge is strong. The shape of the tooth is obtained by recursive detection of the contours. Active contour model is used to obtain the contour where the edge is strong, and closed spline curve is used to represent the contour. Experimental results show that the shape of a tooth can be reconstructed from actual CT images by the proposed method.

Structure extraction from decorated characters using multiscale images

Decorated characters are widely used in various documents. Practical optical character reader is required to deal with not only common fonts but also complex designed fonts. However, since the appearances of decorated characters are complicated, most general character recognition systems cannot give good performances on decorated characters. In this paper, an algorithm that can extract characters essential structure from a decorated character is proposed. This algorithm is applied in preprocessing of character recognition. The proposed algorithm consists of three procedures: global structure extraction, interpolation of structure and smoothing. By using multiscale images, topographical features, such as ridges and ravines are detected for structure extraction. Ridges are used for extracting global structure and ravines are used for interpolation. Experimental results show character structures can be clearly extracted from very complex decorated characters.

A New Approximation Method of the Quadratic Discriminant Function

For many statistical pattern recognition methods, distributions of sample vectors are assumed to be normal, and the quadratic discriminant function derived from the probability density function of multivariate normal distribution is used for classification. However, the computational cost is O(n2) for n-dimensional vectors. Moreover, if there are not enough training sample patterns, covariance matrix can not be estimated accurately. In the case that the dimensionality is large, these disadvantages markedly reduce classification performance. In order to avoid these problems, in this paper, a new approximation method of the quadratic discriminant function is proposed. This approximation is done by replacing the values of small eigenvalues by a constant which is estimated by the maximum likelihood estimation. This approximation not only reduces the computational cost but also improves the classification accuracy.

Absolute expediency of learning automata

Abstract What kinds of structures of automata are necessary and sufficient to adapt to an unknown environment with learning is the problem considered. As a solution, the structures of learning automata known as absolutely expedient automata (a kind of variable-structure stochastic automata redefined here as stochastic vector automata) in a stationary random environment are discussed and identified. Necessary and sufficient conditions for absolute expediency of a class of automata with restricted structures are already known. Several extended conditions, shown here, for automata in a more general class to be absolutely expedient are sufficiently general to imply the known results. An absolutely expedient automaton, hitherto unknown, is proposed as an example of the extended conditions. In addition, several basic results on the behavior of learning automata in an environment are shown, which remove some past misunderstandings and give an idea of the automatas behavior.

Two-stage computational cost reduction algorithm based on Mahalanobis distance approximations

For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computational cost for calculating discriminant function is proposed. This algorithm consists of two stages which are feature vector. Division and dimensional reduction. The processing of feature division is based on characteristic of covariance matrix. The dimensional reduction in the second stage is done by an approximation of the Mahalanobis distance. Compared with the well-known dimensional reduction method of K-L expansion, experimental results show the proposed algorithm not only reduces the computational cost but also improves the recognition accuracy.