کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946119 | 1439269 | 2017 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
End-to-end recognition of slab identification numbers using a deep convolutional neural network
ترجمه فارسی عنوان
به رسمیت شناختن شماره های شناسایی اسلب با استفاده از یک شبکه عصبی کانولوشن عمیق پایان دادن به پایان
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کلمات کلیدی
کاربرد صنعتی، صنعت فولاد، شماره شناسنامه اسلب، شبکه عصبی کانولوشن عمیق، تشخیص متن،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper proposes a novel algorithm for the end-to-end recognition of slab identification numbers (SINs). In the steel industry, automatic recognition of an individual product information is important for production management. The recognition of SINs in actual factory scenes is a challenging problem due to complicated background and low-quality of characters. Conventional rule-based algorithms were developed to extract information of SINs, but these methods require engineering knowledge and tedious work for parameter tuning. The proposed algorithm employs a data-driven method to overcome these limitations and to handle the challenges for the recognition of SINs. This paper proposes accumulated response map and model-based score function to effectively use the outputs of a deep convolutional neural network. Experiments were thoroughly conducted for industrial data collected from an actual steelworks to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that simultaneous recognition of entire characters in a SIN by optimizing the model-based score function is more effective for the robust performance compared to separated recognition of individual characters.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 132, 15 September 2017, Pages 1-10
Journal: Knowledge-Based Systems - Volume 132, 15 September 2017, Pages 1-10
نویسندگان
Sang Jun Lee, Jong Pil Yun, Gyogwon Koo, Sang Woo Kim,