Bay Number Recognition Based on Deep Convolutional Recurrent Neural Network

The recognition of image text sequences has been the subject of long-term research in computer vision. The recognition problem of the shell number image sequence studied in this paper is based on the deep convolutional neural network architecture, and an image recognition algorithm that integrates feature extraction, sequence modeling, and transcription into a unified framework is adopted. The algorithm used has four characteristics: (1) Compared with most algorithms that require separate training and coordination, the method adopted in this paper has end-to-end characteristics. (2) It can handle indefinite length image sequences without involving character segmentation or horizontal-scale normalization. (3) The algorithm is not limited to any predefined vocabulary, and has achieved remarkable performance in both no-lexicon and dictionary-based scene text recognition tasks. (4) The algorithm produces an efficient and much smaller model, which is more practical for real-world scenarios. Using this algorithm for bay number recognition can assist in scene location and provide smart services and application through AI technology.