/ Shunji Mori / Professor
/ Hirobumi Nishida / Associate Professor
/ Tony Y.T. Chan / Assistant Professor
/ Yu Nakajima / Research Associate
First and foremost, the Image Processing Laboratory engages in research and development of image pattern recognition systems. More specifically, as can be seen from the background information and the recent research publications of the members of the laboratory, character recognition is our current focus. Related to the recent involvement of multimedia systems, character recognition has been noted by many researchers and engineers. On the other hand, character recognition techniques are generally divided into off-line and on-line methods. The former is typical in character recognition and aims at duplicating the human ability in recognition. However, on-line character recognition is also noted recently in connection with so-called pen computers. On-line methods provide very flexible, convenient, and natural human-interface. Historically speaking, these techniques have been developed separately. However, both techniques can be developed together in principle. The common approach makes possible recognition flexibility in such a way that the usual constraints being imposed on the on-line techniques can be removed. For example, writing order and number of strokes constituting a character are typical of such constraints. On the other hand, we are requested to develop an inspection system for flaw of Sake bottles. The contribution to the domestic industry is very important mission of our University. Therefore, we are doing a basic research on that together with Industry Application Laboratory. In general, image inspection is a very important theme and has many applications in industry broadly.
In this respect, one of our members, Prof. Nishida, is a world-class to level researcher and engineer. Now he becomes well-known in this field internationally and so has been appointed as a Chairman of the IAPR (International Association for Pattern Recognition) Technical Committee, TC 8 (Application in Industry) in spite of his young age. This is very appropriate considering his engineering background. Actually, an OCR group in which Prof. Nishida played and important role developed a very powerful OCR package when he worked for Ricoh Research and Development Center. One of the members of the OCR group was Mr. Nakajima, who has joined us as a research associate. He is well-acquainted with different computer systems and also works as an instructor in programming. The system developed is called filtering in character recognition, which makes the packages very fast without being hardware-specific. Prof. Nishida and the OCR group were awarded the Excellence in Programming Prize from the Ministry of Post and Telecommunications. The basic configuration of the system was proposed by Prof. Mori when he was head of the Artificial Intelligence Research center of Ricoh.
Naturally, the essential point of the developed system lies in the excellent algorithm of character recognition, which is based on an algebraic approach to shape description. This approach was investigated as basic research by Prof. Mori when he worked for the Electrotechnical Laboratory of the Ministry of International Trade and Industry.
Later the basic line was further developed, in theory and in practice, by Prof. Nishida. We think that this is one of the most elegant theories in character recognition. He is developing his theory further and succeeded in making a very systematic nonlinear deformation model of a standard shape belonging to a class. This model covers almost conceivable deformation of a character shape in the level of Roman alphabet, Katakana and Hirakana, at least. Because of its theoretical richness and high-level abstraction, the model has very broad applications in shape recognition in general. It is applicable to both off-line and on-line character recognition systems, for example. Further extension of the theory is in the application of the recognition of three-dimensional bodies, in which a piece of the surfaces must be represented effectively in terms of pattern recognition and global organization mechanism. In this regard, a set of primitive surfaces must be considered. Furthermore, in addition to basic recognition mechanisms in a bottom-up manner. Rich knowledge concerning " objects" must be naturally employed. Some knowledge representational methods developed in the field of artificial intelligence research are being considered. Assistant Professor Chan is investigating unifying pattern recognition and artificial intelligence approaches based on models proposed by Prof. Lev Goldfarb of the University of New Brunswick, Prof. Nishida, and himself.
On the other hand, any abstract theory must have its raison d'etre in the real world. The proofs are in its utility. In this sense, experiments and/or computer simulation are essential research components. To that end, we need powerful facilities such as observation. display, interface, and calculation systems. To make such an integrated experimental system is also very important research work.
In turn, it can be easily applicable to the top-down education system at the University of Aizu. Actually we have developed such experimental system called IRAPES (Image Recognition And Processing Experiment System) cooperated with Ricoh Software Engineering. This will be further developed in such a way that it contains more application programs.
At any rate, the performance level of current image recognition systems is far below that of the human beings, who can recognize very distorted handwritten characters and degraded noisy printed characters easily without any heuristical learning. This ability is most mysterious. Therefore, from a long-term historical perspective, we are still only at the dawn of the day. Lots of work is available for ambitious young students, researchers, and engineers, in particular. We will pursue this very interesting research field along the lines mentioned.
Refereed Journal Papers
The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some {\em qualitative} features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mori (1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, 1993). However, in the analysis of directional features, the number of directions is fixed to four, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida-Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on $2^m$-directional features ($m=2,3,4,\ldots$) and convex/concave features. On the other hand, software OCR systems without specialized hardware attract much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multi-stage strategy.
Qualitative and global features are appropriate for describing the shape of a complex and deformed object rather than quantitative and local features. In particular, quantized directional features and quasi-convexity/concavity are powerful and flexible for describing shape of handwritten characters. In this paper, we present a method for structural analysis and description of simple (open) arcs or closed curves based on $2m$-directional features ($m=1,2,3,\ldots$) and quasi-convexity/concavity. We show some examples of shape description of handwritten characters and experimental results for selecting the optimal quantization of direction in handwritten character recognition.
An essential problem in handwriting recognition is how to cope with the complex shape deformation, and therefore, the modeling of the deformation and metamorphosis, transformation of an instance of a class into an instance of the other class via continuous transformation, is a key to breaking through the difficulties in handwriting recognition. In this paper, on the basis of the structural feature extraction and description by Nishida, we present a model for structural deformation with simple, local operations that preserve the global structure of the shape. Furthermore, we present an experimental approach to analysis of metamorphosis of character shapes based on the structural deformation model.
On-line recognition algorithms free from writing constraints and high-quality thinning algorithms are important subjects in research on handwriting recognition and are also essential for the integration of off-line and on-line recognition of handwriting. We present an approach to the integration of off-line and on-line recognition of unconstrained handwritten characters by adapting an on-line recognition algorithm to off-line recognition, based on high-quality thinning algorithms. In the experiments, high recognition rate has been attained with a small number of class descriptions, typically one class for one character.
Structural deformation caused by discontinuous transformations is an intractable problem in shape analysis and description. Structural descriptions depend on the topological structure of the shape, and therefore, they are sensitive to discontinuous transformations which change the topology of the shape. Because of the difficulties, there have been few systematic studies for analyzing and modeling structural deformations caused by discontinuous transformations. In this paper, as a first step for overcoming the difficulties, we give a complete and systematic analysis of structural deformations of curves due to some types of commonly occurring discontinuous transformations, in terms of the curve description based on quasi-convexity/concavity incorporating quantized directional features. The transformation laws obtained by the analysis are composed of a small and tractable number of distinct cases. The analysis is applied to the automatic construction of class descriptions from data. In order to show the practical effectiveness of the analysis, experimental results are given for unconstrained handwritten character recognition using a real and standard data set. High recognition accuracy is attained for a variety of deformed patterns with a small number of prototypes (typically one class for one character). We also mention some other applications of the analysis such as the deduction of possible structural descriptions from the class descriptions, and the examination of the status of conflicts among the class descriptions.
The prime difficulty in research and development of handwritten character recognition systems is the variety of shape deformations. The key to recognizing such complex objects as handwritten characters is through shape descriptions which are robust against shape deformation, together with quantitative estimation of the amount of deformation. In this paper, on the basis of the structural description by Nishida and Mori (1992), we propose a shape matching algorithm and a method for analysis and description of shape transformation for handwritten characters. The object is described in terms of a qualitative and global structure which is robust against deformation, and the description is matched against built-in models. On the basis of the correspondence of components between the object and the model, geometrical and statistical transformations are estimated, and the decision of recognition or rejection is based on the estimations. Structural descriptions and geometrical/statistical transforms are integrated in a systematic way. Experimental results are shown for off-line handwritten numeral recognition and on-line handwriting recognition.
We present an approach to automatic construction of structural models incorporating discontinuous transformations, with emphasis on application to unconstrained handwritten character recognition. We consider this problem as constructing inductively, from the data set, some shape descriptions that tolerate certain types of shape transformations. The approach is based on the exploration of complete, systematic, high-level models on the effects of the transformations, and the generalization process is controlled and supported by the high-level transformation models. An analysis of the {\em a priori} effects of commonly occurring discontinuous transformations is carried out completely and systematically, leading to a small, tractable number of distinct cases. Based on this analysis, an algorithm for the inference of super-classes under these transformations is designed. Furthermore, through examples and experiments, we show that the proposed algorithm can generalize unconstrained handwritten characters into a small number of classes, and that one class can represent various deformed patterns.
Handwritten character recognition has been increasing its importance and has been expanding its application areas such as office automation, postal service automation, automatic data entry to computers, etc. It is challenging to develop a handwritten character recognition system with high processing speed, high performance, and high portability, because there is a trade-off among them. In current technology, it is difficult to attain high performance and high processing speed at the same time with single algorithms, and therefore, we need to find an efficient way of combination of multiple algorithms. We present an engineering solution to this problem. The system is based on multi-stage strategy as a whole: The first stage is a simple, fast, and reliable recognition algorithm with low substitution-error rate, and data of high quality are recognized in this stage, whereas sloppily written or degraded data are rejected and sent out to the second stage. The second stage is composed of a sophisticated structural classifier and a pattern matching classifier, and these two complementary algorithms run in parallel (multiple expert approach). We demonstrate the performance of the completed system by experiments using real data.
Refereed Proceeding Papers
An essential problem in handwriting recognition is how to cope with the complex shape deformation, and therefore, the modeling of the deformation and metamorphosis (transformation of an instance of a class into an instance of the other class via continuous transformation) is a key to breaking through the difficulties in handwriting recognition. In this paper, on the basis of the structural feature extraction and description by Nishida, we present a model for structural deformation with simple, local operations that preserve the global structure of the shape. Furthermore, we present an experimental approach to analysis of metamorphosis of character shapes based on the structural deformation model.
An innovative approach to automatic construction of structural models was proposed by Nishida and Mori (1993). This approach classifies shapes according to the representation of continuous transformation, and a class is composed of shapes that can be transformed continuously to each other. However, global structure and features of the shape are changed considerably by shape deformation in unconstrained handwriting, and therefore, a large number of classes are required for dealing with unconstrained handwritten characters. The clues for discontinuous structural transformation lie in deformation around singular points and stroke connection of handwriting. For designing an algorithm for automatic construction of structural models incorporating discontinuous transformations, we analyze systematically how global structure and features are changed according to (1) structural transformation around singular points; (2) stroke connection. Based on the set of systematic laws of shape transformation, we design an algorithm for finding component correspondence between a pair of deformed patterns, and present an algorithm for automatic construction of structural models for unconstrained handwritten characters along with some examples and experimental results. The proposed algorithm can generalize unconstrained handwritten characters into a small number of classes, and one class can represent various deformed patterns.
We present an approach to automatic construction of structural models incorporating discontinuous transformations, with emphasis on application to unconstrained handwritten character recognition. We consider this problem as constructing inductively, from the data set, some shape descriptions that tolerate certain types of shape transformations. The approach is based on the exploration of complete, systematic, high-level models on the effects of the transformations, and the generalization process is controlled and supported by the high-level transformation models. An analysis of the a priori effects of commonly occurring discontinuous transformations is carried out completely and systematically, leading to a small, tractable number of distinct cases. Based on this analysis, an algorithm for the inference of super-classes under these transformations is designed. Furthermore, through examples and experiments, we show that the proposed algorithm can generalize unconstrained handwritten characters into a small number of classes, and that one class can represent various deformed patterns.
Qualitative and global features are appropriate for describing the shape of a complex and deformed object rather than quantitative and local features. In particular, quantized directional features and quasi-convexity/ concavity are powerful and flexible for describing shape of handwritten characters. In this paper, we present a method for structural analysis and description of simple (open) arcs or closed curves based on $2N$-directional features ($N=1,2,3,\ldots$) and quasi-convexity/concavity. We show some examples of shape description of handwritten characters and experimental results for selecting the optimal quantization of direction in handwritten character recognition.
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