Gradient Difference based approach for Text Localization in Compressed domain

TL;DR

This paper introduces a novel gradient difference method for text localization in compressed video frames and images, utilizing wavelet transforms, zero crossing, and morphological operations to accurately detect texts of various styles.

Contribution

The paper presents a new approach combining gradient difference and zero crossing techniques for scene text localization directly in compressed domain images.

Findings

Effective detection of texts of various sizes and fonts

High accuracy demonstrated on standard datasets

Method reduces false positives through combined analysis

Abstract

In this paper, we propose a gradient difference based approach to text localization in videos and scene images. The input video frame/ image is first compressed using multilevel 2-D wavelet transform. The edge information of the reconstructed image is found which is further used for finding the maximum gradient difference between the pixels and then the boundaries of the detected text blocks are computed using zero crossing technique. We perform logical AND operation of the text blocks obtained by gradient difference and the zero crossing technique followed by connected component analysis to eliminate the false positives. Finally, the morphological dilation operation is employed on the detected text blocks for scene text localization. The experimental results obtained on publicly available standard datasets illustrate that the proposed method can detect and localize the texts of various sizes, fonts and colors.