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Subject: 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).
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    Mixed thai-english character classification based on histogram of oriented gradient feature
    (2013-01-01)
    Siriteerakul, Teera
    The task of classifying mixed Thai-English characters carries considerable challenges due to the number and complexity of the characters. This paper proposes and empirically investigates the performance of a classification system that uses Histogram of Oriented Gradient as an image feature with Support Vector Machine as a classification tool. The experiments were done on the datasets provided by NECTEC which consists of over 600,000 printed images of individual characters from 142 distinct classes. With this proposed method, an accuracy of 97% can be achieved without a look up dictionary or any post-processing system. © 2013 IEEE.