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.

中文翻译：

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DetReco：基于深度神经网络的目标文本检测与识别

基于深度学习的对象检测方法已应用于ITS（智能交通系统）和ADS（自动驾驶系统）等各个领域。同时，不同场景下的文本检测和识别也引起了广泛的关注和研究工作。在本文中，我们提出了一种新的对象文本检测和识别方法，称为“ DetReco”，用于检测对象和文本并识别文本内容。该方法由目标文本检测网络和文本识别网络组成。YOLOv3被用作目标文本检测任务的算法，而CRNN被用于处理文本识别任务。我们将通用对象和文本的数据集组合在一起以训练网络。在测试时，检测网络会检测图像中的各种对象。然后，文本图像被传递到文本识别网络以导出文本内容。实验表明，在检测性能方面，该方法对一般物体达到了78.3 mAP（平均平均精度），为文本达到了72.8 AP（平均精度）。此外，所提出的方法能够以鲁棒性检测和识别仿射变换或遮挡的文本。另外，对于在一般对象周围检测到的文本，可以将文本内容用作区分对象的标识符。所提出的方法能够可靠地检测和识别仿射变换或遮挡的文本。另外，对于在一般对象周围检测到的文本，可以将文本内容用作区分对象的标识符。所提出的方法能够可靠地检测和识别仿射变换或遮挡的文本。另外，对于在一般对象周围检测到的文本，可以将文本内容用作区分对象的标识符。