Unsupervised Speech Text Localization in Comic Images

Localizing speech texts in comic images is a crucial step for catering the growing needs of reading comics on mobile devices. For example, automatically reading speech texts while adding sound effects alongside can not only render comic contents vividly but also help visually impaired readers. Unlike conventional text localization methods, we present an effective unsupervised speech text localization method in this paper that is free of training data. The proposed method consists of two major stages: (1) based on the concurrence of characters, the first stage of our method is to generate some of the character strings (a row or column of characters that align horizontally or vertically) from the comic images while the fonts and gaps of the adjacent characters within the character string are also obtained; (2) in the second stage, the obtained fonts and gaps of adjacent characters are used to detect rest of the character strings within the comic image via Bayesian classifier. The proposed method is tested on a dataset consists of 1000 comic images from ten printed comic series and provide satisfactory results.