An Efficient Signature Recognition System Based on Gradient Features and Neural Network Classifier

Melhaoui, Ouafae El; Benchaou, Soukaina

doi:10.1016/j.procs.2021.12.258

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…Melhaoui and Benchaou [10] fixates on the challenges and methodologies associated with offline signature recognition systems due to its complexity compared to the online mode. The study utilizes a proprietary signature database, containing 240 signatures from 12 individuals.…”

Section: Related Workmentioning

confidence: 99%

Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification

Christianto,

Colin,

Gede Putra Kusuma Negara

2024

IJCDS

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Various document types (financial, commercial, judicial) necessitate signatures for authentication. With the advancements of technology and the increasing number of documents, traditional signature verification methods encounter challenges in facing tasks related to verifying images, such as signature verification. This idea is further reinforced by the growing migration of transactions to digital platforms. To that end, the fields of Machine learning (ML) and Deep Learning (DL) offer promising solutions. This study combines Convolutional Neural Network (CNN) algorithms, such as Visual Geometry Group (VGG) and Residual Network (ResNet) or VGG16 and ResNet-50 specifically, for image embedding alongside ML classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest, and Extreme Gradient Boosting (XGBoost). While the aforementioned solutions are usually enough, real life scenarios tend to differ in environment and conditions. This problem leads to difficulty and accidents in the verification process, causing the users to redo the process or even end it prematurely. To alleviate the issue, this study employs optimization methods such as hyperparameter tuning via Grid Search and triplet loss optimization to enhance model performance. By leveraging the strengths of CNNs, Machine Learning classifiers, and optimization techniques, this research aims to improve the accuracy and efficiency of signature verification processes while addressing real-world challenges and ensuring the trustworthiness of electronic transactions and legal documents. Evaluation is conducted using the ICDAR-2011 and BHSig-260 datasets. Results indicate that triplet loss optimization significantly improves the performance of the VGG16 embedding model for SVM classification, notably elevating the Area Under the ROC Curve (AUC) from 0.970 to 0.991.

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Section: Related Workmentioning

confidence: 99%

Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification

Christianto,

Colin,

Gede Putra Kusuma Negara

2024

IJCDS

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“…Fuzzy Min Max Classification Has three layers: an output layer, a hiding layer, and an output layer. [44] Euclidean Distance The actual separation between 2 locations in m-dimensional space, is measured in terms of the vector's true size.…”

Section: K-nnmentioning

confidence: 99%

Advances in Offline Handwritten Signature Recognition Research: A Review

Wang,

Muhammat,

Yadikar

et al. 2023

IEEE Access

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A person's handwritten signature, one of the methods frequently used to confirm their identity, is used exclusively to confirm the biometric identification of different financial, legal, banking, insurance, and other business documents. Signature recognition techniques are used to identify which user someone's signature is affiliated with. In recent years, many researchers have worked on applying new methods to this work, and deep learning methods have become quite prevalent among them. In order to provide more researchers with a better comprehension of how offline handwritten signature recognition work has evolved, the existing approaches, different architectures, challenging issues, and trends within the last 15 years, this paper follows a protocol to organize this work, collects information primarily from studies published in four major databases, applies inclusion and exclusion criteria, reviews offline handwritten signature recognition methods, including issues such as feature extraction and classification, and attempts to summarize the challenges and opportunities in the field. This paper emphasizes the popularity of research directions in this research area in deep learning. In contrast to other surveys in the field, this paper is not limited to a particular phase of work but provides a detailed account of each stage with the expectation that this will help future researchers.INDEX TERMS Offline handwritten signature recognition; deep learning; traditional methods; biometric; review.

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“…Texture feature-based handwriting signature verification methods are commonly used in various systems 6 . Texture features enhance the signature verification process’s accuracy, and maximizes the systems’ performance and effectiveness levels 7 .…”

Section: Introductionmentioning

confidence: 99%

Handwriting identification and verification using artificial intelligence-assisted textural features

Zhao,

2023

Sci Rep

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Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

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An Efficient Signature Recognition System Based on Gradient Features and Neural Network Classifier

Cited by 11 publications

References 12 publications

Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification

Authentic Signature Verification Using Deep Learning Embedding With Triplet Loss Optimization And Machine Learning Classification

Advances in Offline Handwritten Signature Recognition Research: A Review

Handwriting identification and verification using artificial intelligence-assisted textural features

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