An Improved Siamese Network Model for Handwritten Signature Verification

Xiao, Wanghui; Wu, Di

doi:10.1109/icnsc52481.2021.9702190

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…While the SINN algorithm processes one character at a time. So that data consisting of more than one character will be divided by the segmentation process before entering the SINN process [16]. To fulfill the requirements of the data used in the SINN algorithm, the data will first go through a preprocessing process consisting of grayscale, thresholding and resize [17].…”

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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“…According to the characteristics of machine learning, 50 signature samples were randomly selected in this study to train model parameters, and the remaining 5 signature samples were used as validation samples. BHSig260 signature Dataset [36] was divided into the BHSIG-Bengali signature set and BHSIG-Hindi signature set, which were trained and verified independently, respectively. The BHSIG-Bengali signature dataset contains a total of 100 handwritten signature images with Bengali signatures.…”

Section: General Settingsmentioning

confidence: 99%

A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Xiao

Ding

2022

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Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.

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