A Unified Framework for Multioriented Text Detection and Recognition

2014 ◽  
Vol 23 (11) ◽  
pp. 4737-4749 ◽  
Author(s):  
Cong Yao ◽  
Xiang Bai ◽  
Wenyu Liu
Keyword(s):  
Text Detection ◽  
Unified Framework ◽  
Detection And Recognition

scholarly journals DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Fan Zhang ◽  
Jiaxing Luan ◽  
Zhichao Xu ◽  
Wei Chen
Keyword(s):  
Intelligent Transportation Systems ◽  
Research Effort ◽  
Recognition Task ◽  
Autonomous Driving ◽  
Transportation Systems ◽  
Text Detection ◽  
Text Recognition ◽  
Test Time ◽  
Average Precision ◽  
Detection And Recognition

Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.

Sign in / Sign up

Export Citation Format

Share Document