Abstract
Scene text detection is attracting more and more attention and has become an important topic in machine vision research. With the development of mobile IoT (Internet of things) and deep learning technology, text detection research has made significant progress. This survey aims to summarize and analyze the main challenges and significant progress in scene text detection research. In this paper, we first introduce the history and progress of scene text detection and classify the traditional methods and deep learning-based methods in detail, pointing out the corresponding key issues and techniques. Then, we introduce commonly used benchmark datasets and evaluation protocols and identify state-of-the-art algorithms by comparison. Finally, we summarize and predict potential future research directions.
Highlights
Scene text detection (STD) is the process of detecting the presence and position of text in scene images
The authors of this study proposed a method based on mask R-convolutional neural network (CNN), named pyramid mask text detector (PMTD) [14], which used location-aware information to generate text masks instead of binary text masks
conditional spatial expansion (CSE) starts with a seed arbitrarily initialized within a text region and progressively merges neighborhood regions based on local features extracted by a CNN
Summary
Scene text detection (STD) is the process of detecting the presence and position of text in scene images. STD acts as a detection and positioning tool and plays a key role in extracting important high-level semantic information from scene images. It has important applications in intelligent transportation systems [1], content-based image retrieval [2], industrial automation [3], portable vision systems [4,5], etc. We first introduce the definition of the STD and summarize the important features of scene text in natural images. Examples of STD include text detection in various contexts, such as books, ID cards, tickets, intelligent traffic scenarios, such as road detection in various contexts, such as books, ID cards, tickets, intelligent traffic scenarios, such as signs, license plate recognition, etc.
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