A TV logo represents important semantic information of videos. However, its detection and recognition are faced with many problems, including varied categories, complex structures, limited areas, low information content, and severe background disturbance. To improve the generalization ability of the detection model, this study proposes synthesizing TV logo data to construct a training dataset by superimposing TV logo images on background images. Further, a two-stage scalable logo detection and recognition (SLDR) method is put forward, which uses the batch-hard metric learning method to rapidly train the matching model and determine the category of TV logos. In addition, the detection targets can be expanded to unknown categories due to the separation mechanism of detection and recognition in SLDR. The experimental results reveal that synthetic data can effectively improve the generalization ability and detection precision of models, and the SLDR method can achieve comparable precision with the end-to-end model without updating the detection model.