Real-time Scene Text Detection via Connected Component Clustering and Non-text Filtering in videos

Abstract

In this paper, we present a new scene text detection algorithm which does the deblurring of the image in
blurred images and then extracts the text components through connected component extraction techniques. The proposed
image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient
optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not
require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring
algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on
how to select salient edges in a more principle way. In the final latent image restoration step, we develop a simple
method to remove artifacts and render better deblurred images. To be precise, we extract connected components (CCs)
in images by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so
that we can generate candidate regions.