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Author: search.filters.author.Chousangsuntorn, ChousakSubject: search.filters.subject.Character classification

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    A deep learning system for recognizing and recovering contaminated slider serial numbers in hard disk manufacturing processes
    (2021-09-01)
    Chousangsuntorn, Chousak
    ;
    Tongloy, Teerawat
    ;
    Chuwongin, Santhad
    ;
    Boonsang, Siridech
    This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms).