DetReco: Object-Text Detection and Recognition Based on Deep Neural Network
DetReco: Object-Text Detection and Recognition Based on Deep Neural Network
复制标题
DetReco：基于深度神经网络的对象文本检测与识别
DOI：
10.1155/2020/2365076
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发表时间：
2020-07-14
影响因子：
--
通讯作者：
Chen, Wei
中科院分区：
文献类型：
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作者：
Zhang, Fan;Luan, Jiaxing;Chen, Wei
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.
