Adapting Masking Network for Bloom Identification Number Recognition to Different Domains

These days, there are lots of smart factories with automatic systems that improve the factory’s manufacturing efficiency. One of the systems is product identification number recognition. In this study, we handled Bloom Identification Number (BIN) which is common in steel industries. For our BIN recognition algorithm, we adopted deep learning because it outperforms conventional algorithms in many computer vision tasks. Furthermore, applying a trained deep learning model to another factory is a big issue because data from different factories can look alike to us, but the trained models might confuse them because of the difference in background, light condition, and camera position. For this reason, new label annotations are required to train the model once again. However, label annotations will always be a big burden whenever applying a trained model to different factories. In this paper, we introduce a new method of BIN recognition that does not require data labeling of new data when training. This gives us the advantage of eliminating the time of labeling new collected data when applying the deep learning network to other factories.