A barcode is the representation of data including some information related to goods, offered for sale which frequently appears on manufactured items. Especially in the online fashion market such as Poshmark (a second-hand fashion market), barcodes on the tags of the sale items represent the identified information including producer, manufacturer, etc. The market needs a system to automatically detect and decode barcodes in real time. However, the existing methods have some limitations for detecting 1-D barcodes in various backgrounds including tassels, stripes, and clustered text in fashion images. In this research, our focus is on identifying the barcodes in fashion images and distinguishing the barcode from similar non-barcode image content. It is accomplished by applying a Convolutional Neural Network (CNN) to solve this typical objective detection problem. A comparison of the performance between our algorithm and a previous method will be given in our results. Also, a traditional method based on hand-crafted features will be proposed for comparison. For the decoding part, a package including current common types of decoding schemes is used in our work to decode the detected barcodes. But it fails to decode strongly skewed barcode images. Adding pre-processing to warp the skewed images is used to increase the success of decoding.