Discrete Wavelet Transform and Gradient Difference based approach for text localization in videos
This addresses the problem of video text localization for video analysis and retrieval, but it is incremental as it builds on existing image-based methods by incorporating temporal considerations.
The paper tackles text localization in videos by proposing a hybrid method combining discrete wavelet transform and gradient difference, achieving accurate detection across various text sizes, fonts, and colors on standard datasets like ICDAR 2003 and Hua.
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.