An efficient and effective text spotter for characters in natural scene images based on an improved YOLOv5 model

Quanxing Xu; Guanyi Zheng; Wanglong Ren; Xin Li; Zhuo Yang; Zhicheng Huang

doi:10.1117/12.2667388

1 March 2023 An efficient and effective text spotter for characters in natural scene images based on an improved YOLOv5 model

Quanxing Xu, Guanyi Zheng, Wanglong Ren, Xin Li, Zhuo Yang, Zhicheng Huang

Author Affiliations +

Proceedings Volume 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022); 1258809 (2023) https://doi.org/10.1117/12.2667388
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 2022, Chongqing, China

Abstract

Traditional scene text spotters aim to detect and recognize entire words or sentences in natural scene images, however, the detection and recognition of every single character is also as important as the spotting of unifying words or sentences in one image. There are few specialized methods to spot single character in scene text spotting, and some word-based methods can not recognize a series of characters in images if they can not be spelled as a correct word. In addition, some early models can only detect or recognize texts which are horizontal and distinctive. We realize that it is necessary to improve some existing models for achieving the goal of spotting characters, therefore, we propose a novel method based on an improved YOLOv5 model to accomplish the character-level spotting. It’s worth noting that this method can spots characters not only in regular texts but also in irregular texts (curved texts and oriented texts).

Citation Download Citation

Quanxing Xu, Guanyi Zheng, Wanglong Ren, Xin Li, Zhuo Yang, and Zhicheng Huang "An efficient and effective text spotter for characters in natural scene images based on an improved YOLOv5 model", Proc. SPIE 12588, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2022), 1258809 (1 March 2023); https://doi.org/10.1117/12.2667388

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