Complementary combination of holistic and component analysis for recognition of low-resolution video character images

Abstract

Video OCR aims at extracting text from video images in order to understand the context of the video. Video character images are usually given in low resolution with unique characteristics such as large stroke distortion, font variation, and variable size. Therefore, recognizing such characters in video images is very challenging. This is particularly true in the case of Chinese and Korean languages, where characters have complicated shapes and the number of classes (characters) is very large. In this paper, we propose a complementary combination of two recognizer approaches: a holistic approach and a component analysis. The holistic approach utilizes the global shape information of a character image to recognize a radical at a specific location of the character. On the contrary, the component analysis utilizes a detailed local shape of a segmented radical image to recognize the radical. The former is effective for character degradation whereas the latter is strong at processing ambiguous characters and font variations. In an evaluation of 50,000 video character images of Korean script, the proposed method achieved 96.5% accuracy. From this, we may draw a conclusion that the proposed method works well even with low quality images of complicated characters. (c) 2007 Elsevier B.V. All rights reserved.