Abstract

In order to realize accurate camera-based character recognition, machine-readable class information is embedded into each character image. Specifically, each character image is printed with a pattern which comprises five stripes and the cross ratio derived from the pattern represents class information. Since the cross ratio is a projective invariant, the class information is extracted correctly regardless of camera angle. The results of simulation experiments showed that recognition rates over 99 % were obtained by the extracted cross ratio under heavy projective distortions. 1

Multiple classifiers system for reducing influences of atypical observations

Abstract. Atypical observations, which are called outliers, are one of difficulties to apply standard Gaussian density based pattern classification methods. Large number of outliers makes distribution densities of input features multimodal. The problem becomes especially challenging in highdimensional feature space. To tackle atypical observations, we propose multiple classifiers systems (MCSs) whose base classifiers have different representations of the original feature by transformations. This enables to deal with outliers in different ways. As the base classifier, we employ the integrated approach of statistical and neural networks. This consists of data whitening and training of single layer perceptron (SLP). Data whitening makes marginal distributions close to unimodal, and SLP is robust to outliers. Various kinds of combination strategies of the base classifiers achieved reduction of generalization error in comparison with the benchmark method, the regularized discriminant analysis (RDA).

Abstract

For user convenience, document image processing captured with a digital camera instead of a scanner has been researched. However, existing methods of document image processing are not usable for a perspective document image captured by a digital camera because most of them are designed for the one captured by a scanner. Thus, we have to rectify the perspective of the document image and obtain the frontal image as if it was captured by a scanner. In this paper, for eliminating perspective distortion from a planar paper without any prior knowledge, we propose a new rectification method of a document image introducing variants which change according to the gradient of the paper and invariants which do not change against it. Since the proposed method does not use strong assumptions, it is widely applicable to many document images unlike other methods. We confirmed the proposed method rectifies a document image suffering from perspective distortion and acquires the one with affine distortion. 1