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Data Matrix (DM) codes and serialization methods are widely used in various applications, but their readability can be significantly affected by visual defects and degradation. To address this challenge, we propose a novel approach that leverages neural networks to enhance the readability of DM codes. Our method employs an encoder-decoder architecture that processes input RGB images and extracts essential features while preserving relevant details. We demonstrate the effectiveness of our approach through experiments and achieve promising results under challenging conditions. This study contributes to the development of efficient methods for enhancing the readability of DM codes, which has significant practical applications in various fields.

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Optimization of Serialization in the Pharmaceutical Industry Using Artificial Intelligence Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Ahmed Stitou,
  • Adham Chaibi,
  • Ghizlan Bohi,
  • Ismail Lagrat,
  • Driss Serrou,
  • Labiba Bousmaki,
  • Oussama Bouazaoui

摘要

Data Matrix (DM) codes and serialization methods are widely used in various applications, but their readability can be significantly affected by visual defects and degradation. To address this challenge, we propose a novel approach that leverages neural networks to enhance the readability of DM codes. Our method employs an encoder-decoder architecture that processes input RGB images and extracts essential features while preserving relevant details. We demonstrate the effectiveness of our approach through experiments and achieve promising results under challenging conditions. This study contributes to the development of efficient methods for enhancing the readability of DM codes, which has significant practical applications in various fields.