Review of Differentiable Binarization Techniques for Text Detection in Natural Scenes

Abstract

The rich text contained in natural scenes is important for understanding the real world, but the diversity and complexity of natural scene text makes the detection task difficult. With the rise of the intelligent era, deep learning technology has brought breakthroughs for natural scene text detection, and the proposal of differentiable binarization network DBNet has pushed forward the research progress of real-time demand for text detection, and many researchers have carried out innovative and practical researches based on the differentiable binarization technology, and achieved fruitful results. In this paper, the research on text detection algorithms based on differentiable binarization technology in recent years is analyzed in depth. Firstly, the background, working principle, advantages and disadvantages of DBNet model are briefly introduced, and the algorithms based on differentiable binarization technology are classified into five categories of feature extraction, feature fusion, post-processing, overall architecture, and training strategy according to the technical differences. The improvement methods of each category are illustrated in detailed diagrams, the mechanisms of each type of technical method are elaborated in detail, and all methods are analyzed and summarized. Secondly, the commonly used public datasets and text detection performance evaluation indices are introduced, the simulation experimental results of different methods are summarized, and several application scenarios with practical significance are listed. Finally, the future development direction of text detection in natural scenes is considered, and the challenges and problems to be solved are summarized.