29 September 2016 Automatic barcode recognition method based on adaptive edge detection and a mapping model
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An adaptive edge detection and mapping (AEDM) algorithm to address the challenging one-dimensional barcode recognition task with the existence of both image degradation and barcode shape deformation is presented. AEDM is an edge detection-based method that has three consecutive phases. The first phase extracts the scan lines from a cropped image. The second phase involves detecting the edge points in a scan line. The edge positions are assumed to be the intersecting points between a scan line and a corresponding well-designed reference line. The third phase involves adjusting the preliminary edge positions to more reasonable positions by employing prior information of the coding rules. Thus, a universal edge mapping model is established to obtain the coding positions of each edge in this phase, followed by a decoding procedure. The Levenberg–Marquardt method is utilized to solve this nonlinear model. The computational complexity and convergence analysis of AEDM are also provided. Several experiments were implemented to evaluate the performance of AEDM algorithm. The results indicate that the efficient AEDM algorithm outperforms state-of-the-art methods and adequately addresses multiple issues, such as out-of-focus blur, nonlinear distortion, noise, nonlinear optical illumination, and situations that involve the combinations of these issues.
© 2016 SPIE and IS&T
Hua Yang, Lianzheng Chen, Yifan Chen, Yong Lee, Zhouping Yin, "Automatic barcode recognition method based on adaptive edge detection and a mapping model," Journal of Electronic Imaging 25(5), 053019 (29 September 2016). https://doi.org/10.1117/1.JEI.25.5.053019 . Submission:

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