TY - GEN
T1 - Text Recognition in UAV Aerial Images
AU - Wang, Shu
AU - Wang, Dianwei
AU - Han, Pengfei
AU - Ren, Xincheng
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/24
Y1 - 2021/9/24
N2 - Text recognition in unmanned aerial vehicle (UAV) aerial images is an important branch in the field of machine intelligence, which can provide important discriminative information for subsequent applications. At this stage, text recognition methods have made breakthrough progress, but the recognition of distorted and slanted text is still a challenge. In this case, we construct a text recognition network model with correction module, and propose a new type of UAV aerial image text recognition method. Specifically, the model mainly includes two parts: rectification network and recognition network. The rectification network can be optimized without manual annotation, and it can regularize various distorted and inclined UAV image texts. The recognition network introduces the attention mechanism and improves the decoder to perform bidirectional recognition of the rectified UAV image text. In addition, we verify the effectiveness of the rectification network through a large number of experiments, and prove that the model composed of the rectification network and the recognition network can achieve the optimal recognition performance.
AB - Text recognition in unmanned aerial vehicle (UAV) aerial images is an important branch in the field of machine intelligence, which can provide important discriminative information for subsequent applications. At this stage, text recognition methods have made breakthrough progress, but the recognition of distorted and slanted text is still a challenge. In this case, we construct a text recognition network model with correction module, and propose a new type of UAV aerial image text recognition method. Specifically, the model mainly includes two parts: rectification network and recognition network. The rectification network can be optimized without manual annotation, and it can regularize various distorted and inclined UAV image texts. The recognition network introduces the attention mechanism and improves the decoder to perform bidirectional recognition of the rectified UAV image text. In addition, we verify the effectiveness of the rectification network through a large number of experiments, and prove that the model composed of the rectification network and the recognition network can achieve the optimal recognition performance.
KW - Aerial images
KW - Rectification network
KW - Text recognition
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85125860315&partnerID=8YFLogxK
U2 - 10.1145/3488933.3488935
DO - 10.1145/3488933.3488935
M3 - Conference Proceeding
AN - SCOPUS:85125860315
T3 - ACM International Conference Proceeding Series
SP - 232
EP - 238
BT - AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PB - Association for Computing Machinery
T2 - 4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021
Y2 - 17 September 2021 through 19 September 2021
ER -