Abstract: The vehicle identification code (VIN) is of great significance to the annual vehicle inspection. However, due to the lack of character-level annotations, it is impossible to perform the single-character style check on the VIN. To solve this problem, a single-character detection and recognition framework for VIN is designed and a weakly supervised learning algorithm without character-level annotation is proposed for this framework. Firstly, the feature information of each level of VGG16-BN is fused to obtain a fusion feature map with single-character position information and semantic information. Secondly, a network structure for both the character detection branch and the character recognition branch is designed to extract the position and semantic information of a single character in the fusion feature map. Finally, using the text length and single-character category information, the proposed framework is weakly supervised on the vehicle identification code data set without character-level annotations. On the VIN test set, experimental results show that the proposed method realizes the Hmean score of 0.964 and a single-character detection and recognition accuracy rate of 95.7%, showing high practicability.