Vesselness for text detection in historical document images
Simon Hofmann, Martin Gropp, David Bernecker, Christopher Pollin, Andreas Maier, Vincent Christlein · 2016
Text detection is typically the first step for any text processing such as hand-written text recognition, layout analysis, line detection, or writer identification. This paper describes a new method to detect text in images, particularly in historical document images. For a robust detection, we propose the use of the vesselness filter as a new preprocessing step for text detection. We show, that this step improves the detection rate significantly. At the locations segmented by this filter, SIFT keypoints are detected which are spatially clustered. Overlapping windows from these clusters are subsequently VLAD encoded and classified in text and non-text. We evaluate this approach on a newly created database, where we achieve an F1-score of 92%. Additionally, we demonstrate the effectiveness of this method for line segmentation.