School of Computer Science, Yangtze University, Jingzhou 434025, China.
Int J Environ Res Public Health. 2023 Feb 22;20(5):3881. doi: 10.3390/ijerph20053881.
The recognition process of natural scenes is complicated at present, and images themselves may be complex owing to the special features of natural scenes. In this study, we use the detection and recognition of pill box text as an application scenario and design a deep-learning-based text detection algorithm for such natural scenes. We propose an end-to-end graphical text detection and recognition model and implement a detection system based on the B/S research application for pill box recognition, which uses DBNet as the text detection framework and a convolutional recurrent neural network (CRNN) as the text recognition framework. No prior image preprocessing is required in the detection and recognition processes. The recognition result from the back-end is returned to the front-end display. Compared with traditional methods, this recognition process reduces the complexity of preprocessing prior to image detection and improves the simplicity of the model application. Experiments on the detection and recognition of 100 pill boxes demonstrate that the proposed method achieves better accuracy in text localization and recognition results than the previous CTPN + CRNN method. The proposed method is significantly more accurate and easier to use than the traditional approach in terms of both training and recognition processes.
目前自然场景的识别过程较为复杂，并且由于自然场景的特殊性质，图像本身可能较为复杂。在本研究中，我们将药盒文本的检测和识别作为应用场景，设计了一种基于深度学习的自然场景文本检测算法。我们提出了一种端到端的图形文本检测和识别模型，并为药盒识别实现了基于 B/S 研究应用的检测系统，该系统使用 DBNet 作为文本检测框架和卷积循环神经网络 (CRNN) 作为文本识别框架。在检测和识别过程中无需进行图像预处理。后端的识别结果将返回到前端显示。与传统方法相比，这种识别过程降低了图像检测前的预处理复杂性，并提高了模型应用的简单性。对 100 个药盒的检测和识别实验表明，所提出的方法在文本定位和识别结果方面比之前的 CTPN+CRNN 方法具有更好的准确性。在训练和识别过程方面，所提出的方法比传统方法更加准确和易于使用。
