Traditional to transfer learning progression on scene text detection and recognition: a survey

摘要

Many computer vision-based techniques utilize semantic information i.e. scene text present in a natural scene for image analysis. Subsequently, in recent times researchers pay more attention to key tasks such as scene text detection, recognition, and end-to-end system. In this survey, we have given a comprehensive review of the recent advances on these key tasks. The review focused firstly on the traditional methods and their categorization, also show the evolution of scene text detection, recognition methods, and end-to-end systems with their pros and cons. Secondly, this survey focuses on the latest state-of-the-art (SOTA) methods based on transfer learning and additionally do the extension of scene text reading system i.e. salient text detection, text or non-text image classification, a fusion of scene text in vision and language, etc. After that, we have done a performance analysis on various SOTA methods on the various key issues and techniques. Finally, we discuss the various evaluation metrics and standard dataset on which the various SOTA methods of scene text detection is investigated and compared.