| dc.description.abstract | Data-Matrix two-dimensional barcodes present extensive information within a compact area. Data-Matrix barcodes have been widely used in automobile, aerospace, semiconductor,
and electronic components. However, the Data Matrix barcode readers currently available on the market use multiple image preprocessing methods for identification of barcodes that appear fuzzy, unevenly lit, distorted, and otherwise unrecognizable; with such systems, failed
identification often needlessly wastes computational time. Poor image quality or a large amount of unnecessary high-frequency information can hinder the barcode recognition and consume a substantial amount of time. In addition, unrecognizable barcodes cannot be predicted in advance, resulting in wasted time. This study presents a Data-Matrix two-dimensional barcode recognition system based on the mainstream open source software libdmtx, which uses Sobel filter edge detection to obtain the barcode along the positive and negative tangent lines. To address the shortcomings of typical systems, a novel classifier is
applied in this study to predict whether a given barcode can be successfully recognized. This prevents cumbersome image preprocessing and subsequent recognition steps for low-quality barcode images, thereby saving computational time. Furthermore, during the image preprocessing phase, a discrete wavelet transform approach is employed to enhance image quality and improve barcode recognition, thereby improving both speed and accuracy of system operations. | en_US |