Skeleton Filter: A Self-Symmetric Filter for Skeletonization in Noisy Text Images

Abstract

Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images. Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images. The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions. We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton. Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.