Name
Abstract | ||
|---|---|---|
AbstractThe history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition, (2) introduce new insights and ideas, (3) provide a comprehensive review of publicly available resources, and (4) point out directions for future work. In summary, this literature review attempts to present an entire picture of the field of scene text recognition. It provides a comprehensive reference for people entering this field and could be helpful in inspiring future research. Related resources are available at our GitHub repository: https://github.com/HCIILAB/Scene-Text-Recognition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3440756 | ACM Computing Surveys |
Keywords | DocType | Volume |
Scene text recognition, end-to-end systems, deep learning | Journal | 54 |
Issue | ISSN | Citations |
2 | 0360-0300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxue Chen | 1 | 10 | 2.88 |
| Lianwen Jin | 2 | 1337 | 113.14 |
| Yuanzhi Zhu | 3 | 16 | 2.91 |
| Canjie Luo | 4 | 54 | 9.32 |
| Tianwei Wang | 5 | 10 | 4.97 |