Labels are used for managing warehouse environments by collecting information from existing items on shelves and racks. Labels enable description and identification of items accurately in a short time. Although lot of research have been done in the field of barcode detection, the present methods for detection are applicable at a short distance from the camera and with a clear background. Therefore, label detection from captured images is challenging especially with a large and complex background. Once a label is detected, it is ready for next process of recognition, to read out the stored information in texts and barcodes. In this thesis, we compared methods from previous works and implemented the most suitable one for detecting one-dimensional (1D) barcodes available on the captured images by standard lens. We created a dataset for label detection with an assumption on background color and we continued processing by K-means clustering and classification. After localizing label regions, a projection for determining a different candidate area is done. We have worked on two types of barcodes, one-dimensional (1D) and Data Matrix as a two-dimensional (2D) barcode. The results show a good performance of the system in terms of images, which are the most important issue in terms of industrial detection.