We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function. The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and thus degrades recognition ability. Experimental results for printed Chinese character recognition reveal that GLVQ is superior to LVQ in recognition ability.

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Generalized Learning Vector Quantization

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

'Online recognition of Chinese characters: the state-of-the-art

This paper presents a study of using 8-directional features for online handwritten Chinese character recognition. Given an online handwritten character sample, a series of processing steps, including linear size normalization, adding imaginary strokes, nonlinear shape normalization, equidistance resampling, and smoothing, are performed to derive a 64/spl times/64 normalized online character sample. Then, 8-directional features are extracted from each online trajectory point, and 8 directional pattern images are generated accordingly, from which blurred directional features are extracted at 8/spl times/8 uniformly sampled locations using a filter derived from the Gaussian envelope of a Gabor filter. Finally, a 512-dimensional vector of raw features is formed. Extensive experiments on the task of recognizing 3755 level-1 Chinese characters in GB2312-80 standard are performed to compare and discern the best setting for several algorithmic choices and control parameters. The effectiveness of the studied approach is confirmed.

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A study on the use of 8-directional features for online handwritten Chinese character recognition

This paper describes an online Japanese character recognition system using advanced techniques of pattern normalization and direction feature extraction. The normalization of point coordinates and the decomposition of direction elements are directly performed on online trajectory, and therefore, are computationally efficient. We compare one-dimensional and pseudo two-dimensional (pseudo 2D) normalization methods, as well as direction features from original pattern and from normalized pattern. In experiments on the TUAT HANDS databases, the pseudo 2D normalization methods yielded superior performance, while direction features from original pattern and from normalized pattern made little difference.

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Online Japanese Character Recognition Using Trajectory-Based Normalization and Direction Feature Extraction

This paper compares the current state of the art in online Japanese character recognition with techniques in western handwriting recognition. It discusses important developments in preprocessing, classification, and postprocessing for Japanese character recognition in recent years and relates them to the developments in western handwriting recognition. Comparing eastern and western handwriting recognition techniques allows learning from very different approaches and understanding the underlying common foundations of handwriting recognition. This is very important when it comes to developing compact modules for integrated systems supporting many writing systems capable of recognizing multilanguage documents.

The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition

It is shown that an offline character recognition method is effective for use in an online Japanese character recognition. Major conventional online recognition methods have restricted the number and the order of strokes. The offline method removes these restrictions, based on pattern matching of orientation feature patterns. It has been improved with developments in nonlinear shape normalization, nonlinear pattern matching, and the normalization-cooperated feature extraction method. It was used to examine 52,944 online Kanji characters in 1,064 categories. The recognition rate achieved 95.1%, and the cumulation recognition rate within the best five candidates was 99.3%. >

J. Tsukumo

Papers

On-line Japanese character recognition experiments by an off-line method based on normalization-cooperated feature extraction