Discrete Wavelet Transform and Gradient Difference based approach for text localization in videos

TL;DR

This paper introduces a hybrid approach combining discrete wavelet transform and gradient difference for robust text localization in videos, leveraging temporal redundancy and geometric rules to improve accuracy.

Contribution

A novel hybrid method that fuses wavelet transform and gradient difference for effective text detection in videos, addressing challenges posed by natural scene variations.

Findings

Accurately detects texts of various sizes, fonts, and colors.

Effective on standard datasets ICDAR 2003 and Hua.

Suitable for large video collections.

Abstract

The text detection and localization is important for video analysis and understanding. The scene text in video contains semantic information and thus can contribute significantly to video retrieval and understanding. However, most of the approaches detect scene text in still images or single video frame. Videos differ from images in temporal redundancy. This paper proposes a novel hybrid method to robustly localize the texts in natural scene images and videos based on fusion of discrete wavelet transform and gradient difference. A set of rules and geometric properties have been devised to localize the actual text regions. Then, morphological operation is performed to generate the text regions and finally the connected component analysis is employed to localize the text in a video frame. The experimental results obtained on publicly available standard ICDAR 2003 and Hua dataset illustrate that the proposed method can accurately detect and localize texts of various sizes, fonts and colors. The experimentation on huge collection of video databases reveal the suitability of the proposed method to video databases.