A Character-Structure-Guided Approach to Estimating Possible Orientations of a Rotated Isolated Online Handwritten Chinese Character

Abstract

This paper presents a character-structure-guided approach to estimating possible orientations of a rotated isolated online handwritten Chinese character. Using the estimated orientations, the original distorted sample can be transformed to a normal position, which can be recognized more accurately by using a classifier trained from normal-position samples. The effectiveness of this approach is demonstrated by recognizing rotated samples generated artificially from the popular Nakayosi and Kuchibue Japanese character databases, with average recognition accuracies of 96.05%, 97.35% and 99.13% on top-6, top-12, and top-100 candidates, respectively.