Video Text Localization with an emphasis on Edge Features

TL;DR

This paper introduces a robust video text localization method emphasizing edge features using Sobel edge detection, morphological rules, and connected component analysis, achieving effective detection across diverse text styles.

Contribution

The proposed approach uniquely combines edge emphasis with morphological rules for improved text localization in videos and natural scene images.

Findings

Effective detection of texts of various sizes, fonts, and colors.

Robust localization demonstrated on standard datasets.

Improved false positive elimination.

Abstract

The text detection and localization plays a major role in video analysis and understanding. The scene text embedded in video consist of high-level semantics and hence contributes significantly to visual content analysis and retrieval. This paper proposes a novel method to robustly localize the texts in natural scene images and videos based on sobel edge emphasizing approach. The input image is preprocessed and edge emphasis is done to detect the text clusters. Further, a set of rules have been devised using morphological operators for false positive elimination and connected component analysis is performed to detect the text regions and hence text localization is performed. 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.