University of Twente Student Theses


DataMatrix Barcode Read Rate Improvement Using Image Enhancement

Svarnovics, Vladislavs (2021) DataMatrix Barcode Read Rate Improvement Using Image Enhancement.

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Abstract:Almost every product in a supply chain comes with a barcode that can be decoded using special decoding devices. Barcodes are usually classified into one and two-dimensional. One-dimensional barcodes are often used for retail product labeling while two-dimensional barcodes are commonly used for manufacturing, warehousing, and logistics. DataMatrix is a popular type of 2D barcodes that, in the context of this work, was used for post parcel and envelope labeling. It is not always possible to successfully extract the information present in a DataMatrix barcode since the decoding may suffer from various image distortion types including blur, smudge, and deformation. In order to improve the decoding rate, classical binarization methods and modern deep learning enhancement solutions were investigated. More specifically, Otsu, Sauvola, Niblack, and Nick binarizations methods were contrasted against state-of-the-art Unet architectures such as AttUnet and Unet3+. The main research question of this work was to find out to what extent Unet-based architectures outperform binarization methods in terms of the DataMatrix decoding rate. In this paper, 65237 decodable DataMatrix barcode samples were analyzed, where 56580 samples were decoded using the open-source ZXing library. The decoded barcodes served as a training set for the deep learning methods since every decoded barcode could be reconstructed into a distortion-free reference image. The results have revealed that the investigated deep learning methods led to 74% decoding rate improvement, and clearly outperformed binarization methods which achieved a 24% decoding rate on the same test set.
Item Type:Essay (Master)
PrimeVision, Delft, The Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Electrical Engineering MSc (60353)
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