Characters Verification via Siamese Convolutional Neural Network

Wang, Shengke; Lv, Xin; Li, Rui; Yu, Changyin; Dong, Jiwen

doi:10.1109/spac46244.2018.8965605

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The model is a reference in implementing the functions of an algorithm that will be used. The siamese neural network model that will be built aims to provide results from images or Batak characters through a comparison process [12]- [15]. This comparison will go through the process of whether the two images of the Batak script being compared are the same image or not (Figure 2).…”

Section: Siamese Neural Network (Sinn) Modelingmentioning

confidence: 99%

One-shot learning Batak Toba character recognition using siamese neural network

et al. 2023

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Siamese neural network (SINN) is an image processing model that compares the scores of two patterns. The SINN algorithm is a combination of the use of the double convolutional neural network (CNN) algorithm. By combined SINN with a one-shot learning algorithm, we can build an image model without requiring thousands of images for training. The test results from the SINN algorithm and one-shot learning show that this process was successful in matching the two data but was unable to produce labels from the data being tested. Because of this, the researcher decided to continue the implementation process using the CNN algorithm combined with single shot detection (SSD). By using a dataset of 5000, the recognition and translation of the Toba Batak script was successful. The percentage of average accuracy results from CNN and SSD in recognizing Toba Batak characters is 84.08% for single characters and 74.13% for mixed characters. While the percentage of average accuracy results for testing the breadth first search algorithm is 75.725%.

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Section: Siamese Neural Network (Sinn) Modelingmentioning

confidence: 99%

One-shot learning Batak Toba character recognition using siamese neural network

et al. 2023

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“…It is necessary to check whether printed outputs or carved wares are missing or etched by comparing the drawings in the printing and carving industries. To reduce the workforce cost and working hours, Wang et al presented an effective method of character verification to automatically compare the similarities between the drawing characters and scanned physical characters based on the Siamese network [39].…”

Section: Deep Siamese Neural Networkmentioning

confidence: 99%

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

et al. 2021

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Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

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Characters Verification via Siamese Convolutional Neural Network

Cited by 2 publications

References 17 publications

One-shot learning Batak Toba character recognition using siamese neural network

One-shot learning Batak Toba character recognition using siamese neural network

Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

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