On-line handwriting has been shown to be an effective tool for the input of Chinese characters. However, due to the high variabilities of handwriting, the recognition performance is somewhat limited. One approach to resolve this problem is to view the pattern mismatch as composed of two components: natural distortion component and residual discriminative component. By adopting a suitable deformation technique, the residual discriminative component can be extracted, which is then used for character recognition. The stroke-based affine transformation (SAT) is a prominent example of this approach. Although the SAT method proved to be successful, it did not fully exploit the structural nature of Chinese characters. This work extends Wakahara's SAT further by considering a higher-level character element, radical, which is commonly deemed as a true structural entity in Chinese characters. The proposed scheme contains two main parts: fuzzy stroke grouping and radical-based affine transformation (RAT). Simple yet effective fuzzy connectivity and transitivity concepts are adopted to extract pseudo-radicals. After the pseudo-radicals are found, an RAT is employed to deform each pseudo-radical so that the input test pattern can match the desired reference pattern more closely. A small-scale experiment was conducted to examine the feasibility of this approach. The results indicate some improvements of the presented approach over the SAT scheme and verify its applicability. Further performance improvement is expected if a writer has the tendency to write radicals more independently.
['Yuan-Bi Lai', 'Leu-Shing Lan', 'Ming-Yen Tsai', 'Chien-Chung Chin']
On-line handwritten Chinese character recognition using a radical-based affine transformation