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SUMMARY This paper presents a model and its effect for on-line handwritten Japanese text recognition free from line-direction constraint and writing format constraint such as character writing boxes or ruled lines. The model evaluates the likelihood composed of character segmentation, character recognition, character pattern structure and context. The(More)
This paper describes a robust context integration model for on-line handwritten Japanese text recognition. Based on string class probability approximation, the proposed method evaluates the likelihood of candidate seg-mentation–recognition paths by combining the scores of character recognition, unary and binary geometric features, as well as linguistic(More)
— This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese characters. It also presents updated evaluation using a large testing set. The model extracts feature points along the pen-tip trace from pen-down to pen-up and sets each feature point(More)
— This paper describes effects of a large amount of artificial patterns to train an on-line handwritten Japanese character recognizer. In general, the more learning patterns employed for training pattern recognition systems, the higher recognition rate is obtained. In reality, however, the existing pattern samples are not enough, especially for languages of(More)
—This paper describes effective object function design for combining on-line and off-line character recognizers for on-line handwritten Japanese text recognition. We combine on-line and off-line recognizers using a linear or nonlinear function with weighting parameters optimized by the MCE criterion. We apply a k-means method to cluster the parameters of(More)
This paper presents a formalization of an on-line writing-box free, line-direction free handwritten Japanese text recognition and its effect. By normalizing character orientation, even text of arbitrary character orientation can be recognized. The method evaluates the likelihood composed of character segmentation, character recognition, character pattern(More)
This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional features from on-line strokes of handwritten text and applies the SVM to the extracted features to produces segmentation point candidates. We(More)
—This paper describes a method for constructing the most efficient and robust coarse classifier from a large number of basic recognizers which are obtained by different parameters of feature extraction, different discriminant methods or functions, and so on. The architecture of the coarse classification is a sequential cascade of basic recognizers and(More)
This paper proposes a " structuring search space " (SSS) method aimed to accelerate recognition of large character sets. We divide the feature space of character categories into smaller clusters and derive the centroid of each cluster as a pivot. Given an input pattern, it is compared with all the pivots and only a limited number of clusters whose pivots(More)