【摘要】 为了获取铭牌图像中的基本参数信息,提出一种基于深度学习的端到端文本识别模型TDRN(Text Detection and Recognition Network)。模型避免了图像裁剪和字符分割,将文本看作一个序列,使用BLSTM(Bidirectional Long Short-term Memory)来获取上下文关系。同时,将文本检测和文本识别整合在同一个网络中共同训练,共享卷积层,以提高整体性能,在文本识别中还引入了注意力机制。模型在公共场景文本数据集SVT(Street View Text)上测试表现良好,F值为68. 69%,高于一般的端到端文本识别模型。与传统铭牌识别方法相比,TDRN准确率更高,鲁棒性更强,能适应复杂的电力场景变化。更多还原

【Abstract】 In order to obtain the basic parameter information in the nameplate image,an end-to-end text recognition model TDRN(Text Detection and Recognition Network) based on deep learning is proposed. The model can avoid image from cropping and character segmentation while text is treated as a sequence and BLSTM(Bidirectional Long Short-term Memory) is used to capture context.Meanwhile,text detection and text recognition are integrated in same network to be trained together and share the convolution layers to improve overall performance. Additionally,attention mechanism is introduced in text recognition. The model performs well on the public scene text dataset SVT(Street View Text) with an F value of 68. 69%,which is higher than that of the general end-to-end text recognition models. Compared with the traditional nameplate recognition methods,the TDRN has higher accuracy and stronger robustness,which is able to adapt to the complicated and changingelectricity scenes.更多还原