A barcode is the representation of data including
some information related to goods, offered for sale, which frequently appears
in on-line fashion images. Detecting and decoding barcode has a variety of
applications in the on-line marketplace. However, the existing method has limitation in detecting barcode
in some backgrounds such as Tassels, strips, and texture in fashion images. So, our work focuses on identifying the barcode
region and distinguishing a barcode from its patterns that are similar to it.
We accomplish this by adding a post-processing technique after morphological
operations. We also apply a Convolutional Neural Network (CNN) to solve this
typical object detection problem. A comparison of the performance between our
algorithm and a previous method will be given in our results. For decoding
part, a package including current common types of decoding scheme is used in
our work to decode the detected barcode. In addition, we add a pre-processing
transformation step to process skewed barcode images in order to improve the
probability of decoding success.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8041415 |
| Date | 14 May 2019 |
| Creators | Qingyu Yang (6634961) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Barcode_Detection_and_Decoding_in_On-line_Fashion_Images/8041415 |
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