Korean-English bilingual videotext recognition for news headline generation based on a split-merge strategy

This paper deals with Korean-English bilingual videotext recognition for news headline generation. Because videotext contains semantic content information, it can be effectively used for understanding videos. Despite its usefulness, it is a challengeable task to apply text recognition technologies to practical video applications because of the computational complexity and recognition accuracy. In this paper, we propose a novel Korean-English bilingual videotext recognition method to overcome the computational complexity as well as achieve comparable recognition accuracy. To recognize both Korean and English characters effectively, the proposed method employs an elaborate split-merge strategy in which the split segments are merged into characters using the recognition scores. Moreover, it avoids unnecessary computation using geometric features such as squareness and internal gap, and thus its computational overhead is remarkably reduced. Therefore, the proposed method is successfully employed in generating news headlines. The effectiveness and efficiency of the proposed method are verified by extensive experiments on a challenging database containing 51,290 text images (176,884 characters).