## 1 Introduction  
At present, advancements in computer vision technology, like license plate recognition, have made everyday life significantly more convenient. With the popularity of mobile devices, such as smartphones, anyone can now easily obtain images and videos of natural scenes using cell phones or digital cameras and share them on the Internet. Among the many objects contained in these images and videos, textual information has an important role. Text information contains rich, precise, and high-level information that gives meaning to the objects in natural scenes, helping people better access and understand the information within images and videos [1]. Therefore, acquiring textual content from images of natural scenes has become a necessary task for machine learning.  
A large amount of text in natural scenes contains a wealth of information that can be used to improve people's productivity and facilitate daily life. For example, text on street signs contains geographic location information, which can be used to locate and navigate vehicles; text on store signs contains information on the store's services, which helps customers quickly decide which store to enter; text on product packaging contains detailed information about the products, helping consumers determine, for instance, whether the products are expired. In the face of massive quantities of image data, how to efficiently obtain text from images has become an active area of recent research.  
Text detection and recognition technologies for the natural scene of the text in street signs have several applications: (1) for autonomous vehicles, using text information from street signs can assist in vehicle positioning and navigation; (2) for intelligent transportation systems, text information from street signs is an important part of the intelligent transportation system, it can provide geographic location information for the establishment of the intelligent transportation systems; (3) for automated data entry, when constructing an intelligent transportation system's database, intelligent detection and recognition of street sign text can automate traffic data entry, improving productivity and replacing labor-intensive manual entry; (4) for intelligent translation and text-to-speech technology, detecting and recognizing street sign text can greatly assist in navigation for the sight-impaired or people who do not speak a local language, thus facilitating daily life. As the number of vehicles continues to increase, traffic congestion has become a major problem for many cities, and intelligent transportation systems and autonomous driving technologies present themselves as effective solutions. Because of the importance of street sign text detection and recognition for intelligent transportation systems, autonomous driving technology, and helping determine the general utility of text recognition, a study was conducted to make a feasible proposal for text detection in street sign scenes.  
The sections of this paper are structured as follows: Section 1 introduces the background and significance of research on text detection in natural scenes and street scenes; Section 2 analyzes the challenges and existing results of text detection in natural scenes and street sign scenes; Section 3 proposes corresponding points for improvement based on the shortcomings of the EAST model; Section 4 analyzes and verifies the modified EAST model through experimental results and comparison with the original EAST model on the ICDAR2015 dataset [2] and the SSTD dataset proposed in this paper; and finally, Section 5 summarizes this paper's findings and proposes directions for future research.