Neural 1D Barcode Detection Using the Hough Transform

• Zamberletti Alessandro

  University of Insubria, Department of Theoretical and Applied Science

• Gallo Ignazio

  University of Insubria, Department of Theoretical and Applied Science

• Albertini Simone

  University of Insubria, Department of Theoretical and Applied Science

• Noce Lucia

  University of Insubria, Department of Theoretical and Applied Science

抄録

Barcode reading mobile applications to identify products from pictures acquired by mobile devices are widely used by customers from all over the world to perform online price comparisons or to access reviews written by other customers. Most of the currently available 1D barcode reading applications focus on effectively decoding barcodes and treat the underlying detection task as a side problem that needs to be solved using general purpose object detection methods. However, the majority of mobile devices do not meet the minimum working requirements of those complex general purpose object detection algorithms and most of the efficient specifically designed 1D barcode detection algorithms require user interaction to work properly. In this work, we present a novel method for 1D barcode detection in camera captured images, based on a supervised machine learning algorithm that identifies the characteristic visual patterns of 1D barcodes' parallel bars in the two-dimensional Hough Transform space of the processed images. The method we propose is angle invariant, requires no user interaction and can be effectively executed on a mobile device; it achieves excellent results for two standard 1D barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the performance of a state-of-the-art 1D barcode reading library by coupling it with our detection method.

収録刊行物

• Information and Media Technologies

  Information and Media Technologies 10 (1), 157-165, 2015

  Information and Media Technologies 編集運営会議

キーワード

• barcode detection
• 1D barcode
• Hough Transform
• Neural Network
• Multilayer Perceptron

詳細情報

• CRID: 1390001205265238912
• NII論文ID: 130004946344
• DOI: 10.11185/imt.10.157
• ISSN: 18810896
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可