Discrimination of similar characters using nonlinear normalization based on regional importance measure

作者：Ryu Sangjin; Kim In Jung^*

来源：International Journal on Document Analysis and Recognition, 2014, 17(1): 79-89.

DOI：10.1007/s10032-013-0206-3

• Pair-wise discrimination
• Discriminative normalization
• Nonlinear normalization
• Handwritten character recognition
• Handwritten Hangul recognition

Discrimination of confusing characters is very important in recognition of character sets containing a multitude of similar characters. Confusing characters have very similar shapes and are separated by only a small difference. For a successful discrimination, we need to focus on that difference. However, the small difference can be reduced or even lost during the feature extraction process. In such a case, further analysis after the feature extraction rarely succeeds. This paper proposes a discriminative nonlinear normalization algorithm to improve discrimination ability. The proposed method emphasizes the difference between confusing characters. It measures the importance of each region in the discrimination of confusing characters. Then, it resamples the image according to the regional importance measure. As a result, it expands important regions but shrinks less important regions. Since it emphasizes important regions in the preprocessing step, it does not suffer from the information loss during the feature extraction. In experiments, the proposed method successfully detected and expanded important regions. In handwritten Hangul recognition, the proposed method outperformed other two recently developed pair-wise discrimination methods. On SERI95a data set, it improved the recognition rate from 87.69 to 90.11 %, achieving a 19.66 % error reduction rate.

• 出版日期2014-3