Character recognition performance improvement using personal handwriting characteristics

The characters written by the same writer are expected to have the following two characteristics. (1) The characters belonging to the same category have similar shapes. (2) There is a shape correlation among characters belonging to different categories. This paper is is aimed at recognition performance improvement using these characteristics. First, this paper describes a method to verify these personal handwriting characteristics using transformed features through principal component analysis. Next, based on the idea that a misrecognized character has an unnatural shape relation with other characters recognized correctly, this paper describes two methods to detect such unnaturalness, which are "within category" detection and "between category" detection. Recognition performance has been improved significantly, especially when unnaturalness is combined with the distances obtained in the recognition process.

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