Canny Text Detector: Fast and Robust Scene Text Localization Algorithm

Hojin Cho, Myungchul Sung, Bongjin Jun

Abstract:

This paper presents a novel scene text detection algorithm,
Canny Text Detector, which takes advantage of the
similarity between image edge and text for effective text localization
with improved recall rate. As closely related edge
pixels construct the structural information of an object,
we observe that cohesive characters compose a meaningful
word/sentence sharing similar properties such as spatial
location, size, color, and stroke width regardless of language.
However, prevalent scene text detection approaches
have not fully utilized such similarity, but mostly rely on the
characters classified with high confidence, leading to low
recall rate. By exploiting the similarity, our approach can
quickly and robustly localize a variety of texts. Inspired by
the original Canny edge detector, our algorithm makes use
of double threshold and hysteresis tracking to detect texts
of low confidence. Experimental results on public datasets
demonstrate that our algorithm outperforms the state-ofthe-
art scene text detection methods in terms of detection
rate.