Recognizing Text Elements for SVG Comic Compression and Its Novel Applications

Abstract

SVG (scalable vector graphics) has become the standard format for 2D graphics in HTML5. Although some image-to-SVG conversion systems had been proposed, the sizes of files they produced are still large. In [1], we proposed a new system to convert raster comic images into vector SVG files. The compression ratio is better than the previous methods. However, these methods do not process text in raster images. In this paper, we improve our system to recognize text elements in the comic and use these text elements to provide better compression and novel applications. The proposed method uses SCW (sliding concentric windows) and SVM (support vector machine) to identify text regions. Then, OCR (optical character recognition) is applied to recognize text elements in those regions. Instead of encoding the text regions as vectors, the text elements are embedded in the SVG file along with their coordinate values. Experimental results show that we can reduce the file sizes to about 52% of the original SVG files. Using these text elements, we can translate comics into other languages to provide multilingual services easily. Text/content-based image search can be supported efficiently. It can also provide a novel application system for story teller.