Detecting Text in Manga Using Stroke Width Transform

Abstract

The Japanese comic-book style known as manga is becoming a popular topic for researchers. This paper focuses on the problem of detecting text regions in manga pages. Because it is time-consuming and laborious to identify the text regions in images manually, an automatic approach is highly desirable. Here, we propose a new text-detection method for manga using a Stroke Width Transform (SWT) technique in conjunction with a Support Vector Machine (SVM). Conventional SWT-based text-detection techniques perform poorly with manga because both text and non-text objects have similar characteristics for strokes, lines, and shapes. To better suit manga, we propose modifying the rules for finding letter candidates, which improves the ability to capture text. An SVM is then used to classify image patches into letter and nonletter regions. We compared our proposed framework with a conventional framework and other text-detection methods including deep-learning techniques. In the results, our proposed method achieved the highest F-measure of 0.506.