Refereed Publications

1. Toru Wakahara, “Multi-template GAT Correlation for Character Recognition with a Limited Quantity of Data,” in Proceedings of the 8^th International Conference on Document Analysis and Recognition, Vol.2, August 2005, pp. 824-828.
   Abstract - This paper addresses the problem of how to construct a robust character classifier when statistical pattern recognition techniques fail because of a limited quantity of data. The key ideas are two ways. One is to adopt a distortion-tolerant shape matching technique. Here, we use an affine-invariant matching technique of global affine transformation (GAT) correlation to absorb linear distortion between grayscale images. The other is to prepare multiple templates for dealing with nonlinear distortion or topologically different shapes. For this purpose K-means clustering is applied to a given limited data in a simple gradient feature space. Recognition experiments using the handwritten numeral database IPTP CDROM1B show that the proposed method achieves a much higher recognition rate of 97.2% as compared to that of 85.8% obtained by the conventional, simple correlation matching with a single template per category.
2. Minoru Yokobayashi and Toru Wakahara, “Segmentation and Recognition of Characters in Scene Images Using Selective Binarization in Color Space and GAT Correlation,” in Proceedings of the 8^th International Conference on Document Analysis and Recognition, Vol.1, August 2005, pp. 167-171.
   Abstract - This paper proposes a new technique of segmentation and recognition of characters with a wide variety of image degradations and complex backgrounds in natural scenes. The key ideas are twofold. One is segmentation of character and background by local/adaptive binarization of one of Cyan/Magenta/Yellow (CMY) color planes with the maximum breadth of histogram. The other is affine-invariant grayscale character recognition using global affine transformation (GAT) correlation. In experiments, we use a total of 698 test images extracted from the public ICDAR 2003 robust OCR dataset containing images of single characters in natural scenes. In advance, we classify those images into seven groups according to the degree of image degradations and/or background complexity. On the other hand, we prepare a single-font set of 62 alphanumerics for templates. Experimental results show an average recognition rate of 70.3%, ranging from 95.5% for clear images to 24.3% for little-contrast images.
3. Yusuke Ojima, Satoshi Kirigaya, and Toru Wakahara, “Determining Optimal Filters for Binarization of Degraded Grayscale Characters Using Genetic Algorithms,” in Proceedings of the 8th International Conference on Document Analysis and Recognition, Vol.2, August 2005, pp. 555-559.
   Abstract - Optimal binarization of degraded grayscale characters is a crucial step to subsequent character recognition. This paper proposes a new, promising binalization technique of grayscale characters using genetic algorithms (GA) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. First, we classify degraded samples of grayscale characters into several categories. Then, in the learning stage, by selecting a training sample from each degradation category we apply GA to the combinatorial optimization problem of determining a sequence of filters that maximizes the fitness value between the filtered training sample and its target image ideally binarized by humans. Finally, in the testing stage, we apply the optimal sequence of filters thus obtained to remaining test samples for each degradation category. Experiments using the public ICDAR 2003 robust OCR dataset demonstrate promising results of binarization of grayscale characters against a wide variety of degradation causes.
4. Yoshimasa Kimura, Toru Wakahara, and Akira Tomono, “Combination of Statistical and Neural Classifiers for a High-Accuracy Recognition of Large Character Sets,” The Journal Systems and Computers in Japan, Vol.36, No. 9, pp. 97-107, August 2005.
   Abstract - In this paper the authors propose a method for high-accuracy recognition of large character sets using a new combination of a statistical method and neural networks. In their method, a hierarchical structure that has several neural networks arranged in a line after the statistical method is used. First, recognition using a statistical method is performed, and this represents the final result if the top candidate does not belong to a predefined set of similar characters. If it does, then the input character is discriminated in a neural network which designates the top candidate by determining the similar characters. The results are output as final results. The basic idea of this method is the functional division of a statistical method and neural networks, and the use of a neural network as determined by a statistical method. The results of recognizing 3201 character types including JIS-1 Kanji showed an improvement in the correct recognition rate due to the combined use of a statistical method and neural networks, thereby demonstrating the validity of the authors' approach.

Other Publications

1. Yosuke Ninomiya, Norio Ikeda, Kousuke Yamazaki, and Toru Wakahara, “Study on Detection and Recognition of Faces in Images Using Eigenspace and GAT Correlation,” Reports of the 216th Technical Conference of the Institute of Image Electronics Engineers of Japan, No.04-07-01, March 2005.
2. Shinya Makino and Toru Wakahara, “Affine-Invariant GAT Correlation Matching of Face Images,” Journal of Japanese Academy of Facial Studies, Vol. 5, No. 1, p. 176, Oct. 2005.

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