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Summary

Machine vision for product text recognition has advanced significantly, with deep learning neural networks (DNNs) now overcoming common challenges in reading manufacturing markings on packaging. This evolution improves pharmaceutical and food product handling.

Keywords:

Character Recognition Deep Learning Imaging System Machine Vision Manufacturing OCR Packaging Product

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Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Manufacturing markings are crucial for handling pharmaceuticals and perishable foods.
Current methods include optical character recognition (OCR) and neural networks.
Recognizing these texts presents significant challenges in real-world applications.

Approach:

Systematic review of machine vision methods for product text recognition (2012-2020).
Searched Science Direct/SCOPUS and Google Scholar.
Included 10 studies detailing recognition methods, performance, imaging, and product/text specifics.

Key Points:

Machine vision methods have evolved, particularly in the last two years.
Deep learning neural network (DNN) architectures, like convolutional neural networks (CNNs), excel at feature extraction from packaging images.
Recent advancements utilize sequential DNNs for text area detection and character recognition, addressing common difficulties.

Conclusions:

Limited studies met review criteria, focusing on machine vision for manufacturing mark recognition.
Methods evolved from OCR to advanced deep learning approaches.
Deep learning enables robust text recognition despite common industry challenges.