Handwritten Signature Verification Using Convolution Neural Network (CNN)

Online and Offline Signature Verification: A Combined Approach

Biometric technologies, such as handwritten signature verification, are extremely useful for identifying individuals inside an organization or finance department. The improvement of picture categorization using deep learning (DL) neural networks has offered an opportunity to exhibit computer vision in contemporary research applications by applying image processing approaches. Manual signature verification is inefficient, error-prone, time-consuming, and inconvenient; therefore, it is critical to create an automatic signature verification recognition system. This research offers an automatic recognition method based on DL that makes use of the Grupo de Procesado Digital de Seales. The biggest publicly accessible handwritten signature dataset, the synthetic signature dataset, was used to classify the signatures of 100 people, each of whom possessed 24 genuine signatures and 30 forged signatures. An inception V3 transfer learning (TL) model is proposed by hyper-tuning different layers from the middle of its architecture and this model is fine-tuned by adding layers, such as flatten, dense (1024), dropout (0.5), and dense (1). The suggested model was tested against six well-known pre-trained TL convolutional neural network models: VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, and EfficientNet. The suggested model surpasses the pre-trained models. Precision, sensitivity, and F1-score are likewise outperformed by the model, with the values of 88%, 88%, and 87%, respectively. The accuracy of the pre-trained models was evaluated as 80%, 81%, 77%, 73%, 71%, and 74%, respectively. The suggested fine-tuned inception V3 gives the highest accurate classifications, distinguishing between genuine and forged signatures with an accuracy of 88%. This study will aid researchers in developing more effective CNN-based models for offline signature verification with application to computer vision.

Handwritten signature verification is a critical aspect of personal authentication in numerous sectors, including banking, legal, and access control systems. This paper provides a comprehensive investigation into writer-dependent signature verification techniques, spanning from traditional approaches such as Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) to cutting-edge deep learning architectures like Siamese and triplet networks. Through extensive experimentation and meticulous analysis conducted on benchmark datasets, we rigorously compare and evaluate the efficacy of these approaches. Our study reveals intriguing insights into the performance characteristics of different methodologies, shedding light on their strengths, weaknesses, and areas for improvement. Notably, we observe that while traditional feature-based methods excel in capturing discriminative information from signature images, deep learning architectures offer enhanced flexibility and adaptability, particularly in scenarios with large and diverse datasets. By combining the strengths of both paradigms, we demonstrate the potential for building robust and reliable signature verification systems capable of handling real-world challenges and variations. We highlight the importance of carefully curated datasets and well-designed network architectures in achieving optimal results, thereby providing valuable guidelines for practitioners and researchers in the field. In conclusion, this study contributes significant insights to the advancement of handwritten signature verification systems, emphasizing the importance of leveraging both traditional methodologies and modern deep learning techniques for building secure and dependable authentication mechanisms. By addressing key challenges and exploring novel approaches, our research aims to foster innovation and enhance the security measures associated with handwritten signature authentication in diverse applications. Keywords—Handwritten signature verification, Writer-dependent approach, Feature-based methods, Deep learning, Siamese networks, Triplet networks, Benchmark datasets, Performance evaluation, Authentication mechanisms, Security measures.

Handwritten Signature Verification (HSV) systems are widely used to verify the authenticity of a user signature automatically. In offline systems, the handwritten signature (represented as an image) is taken from a scanned document, while in online systems, pen tablets are used to register the signature, characterized by several dynamics (e.g., its position, pressure and velocity). The main contribution of this work is to present a new online HSV system that is designed to run on low-end mobile devices. Towards this end, we report the results of an experimental evaluation of our system on different online handwritten signature datasets with low-end mobile devices.

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries.

The scrutiny, identification, and verification of handwritten signatures on Certificates of Origin (C/O) is a critical task for customs authorities in preventing trade fraud. This task remains a significant challenge due to limitations in manpower and the need for manual verification amidst a vast volume of documents. Deep learning (DL) algorithms offer a valuable solution to address this issue. This paper deploys a Siamese Neural Network (SNN) model to assist customs officials in identifying and verifying handwritten signatures on C/O. The results demonstrate the superior performance of the SNN model over conventional Convolutional Neural Network (CNN) models and Machine Learning (ML) models, with accuracy, precision, recall, F1-score, and AUC values of 0.943, 0.912, 0.899, 0.905, and 0.919, respectively. The paper also provides in-depth analyses of omission cases and suggests applications of the model to support the work of customs officials.

The use of a person's signature is considered as one of the most commonly used biometric methods for recognition, either as a standalone feature or as part of multimodal systems. Based on the inter-writer variability, handwritten signatures have been accepted as a personal trait in many transactions to verify the consent or the author's presence. The main challenges for handwritten signature verification are the limited amount of available data for each writer and the intra-writer variability. Sparse Representation (i.e. dictionary learning and sparse coding) achieved state of the art results at handwritten signature verification, using only a small set of genuine reference samples of the writer. The extension of Sparse Representation to an efficient multi-layer architecture is Deep Sparse Coding. Deep Sparse Coding architecture connects multiple layers of Sparse Representation with a sparse-to-dense module in order to enable Sparse Coding on deeper layers. Learning sparse representations at different levels of abstraction leads to building feature hierarchies. The sparse-to-dense module involves a Dimensionality Reduction process, which was implemented with simple and fast methods, such as Random Orthogonal projection or PCA; instead of the computational expensive DrLIM. The performance of the proposed Deep Sparse Coding architecture to the challenging problem of offline signature verification is demonstrated in the popular CEDAR signature dataset, delivering improved state of the art results.

In this digital world, the problem of recognizing a person is resolved by various biometric applications. Along with various unique physical biometric patterns like face, iris, fingerprints, etc., some of the behavioural biometric patterns like signatures, voice, gait, etc. are also used for recognizing an individual. Among all these, handwritten signatures play a major role in our daily life. In this paper, we have discussed the recent work done on online and offline modes which are the two ways of digitizing the handwritten signatures. We have done a comparison of these works based on various features and techniques used. The raw features such as the image of signature and position coordinates, time, pressure, etc. are considered in offline and online signature verification, respectively. For matching the signature pattern, many Artificial-Intelligence-based techniques and machine learning algorithms like support vector machine, hidden Markov model, neural network, fuzzy logic, deep learning, etc. have been used in developing different handwritten signature verification systems. We have also highlighted some signature verification systems which considered handwritten signatures of different languages like Arabic, Chinese, Japanese, Bangla, Hindi, English, etc. The performances of such signature verification systems lead us to do more work using other Indic-scripts-based signatures.

Signatures are commonly used as a unique way of identifying and verifying a person’s identity. Legal documents like bank cheques and transactions, government documents, and affidavits require signature-based verification to identify the authenticity of the person. The signature identification and verification system help to distinguish whether an input signature is honest or a fake. For very long years, this was considered to be a challenging mission when the offline mode was taken into consideration. The offline mode uses scanned signature images, where the online mode of signing process isn't existing. To overcome the signature verification challenge, an application of deep learning techniques to identify feature representations from signature images can be leveraged. In this paper, we have demonstrated how to identify valid and invalid signatures by building a signature verification and identification system that uses deep convolution neural network (ConvNet).KeywordsDeep learningConvolution neural network (ConvNet)Signature verification

Dynamic and static feature fusion for increased accuracy in signature verification

In the evolving landscape of financial transactions, ensuring the security and authenticity of signature verification on bank cheques is vital to prevent fraud. This paper presents a survey of techniques used in signature verification, covering traditional image processing methods such as feature extraction, edge detection, and shape analysis, alongside modern deep learning approaches like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models. The survey explores key challenges, including handwriting variations, image noise, and the need for reliable real-world systems. Solutions such as pre-processing techniques, data augmentation, and transfer learning are discussed to improve accuracy. Commonly used datasets for training and evaluating models are reviewed, highlighting their features and limitations. The paper also considers ethical concerns such as fairness, transparency, and the broader implications of deploying such systems in financial institutions. By integrating insights from both traditional and advanced methods, this survey provides a valuable reference for researchers and practitioners aiming to enhance the accuracy, robustness, and reliability of signature verification systems. Strengthening these systems contributes to the overall integrity and trust in digital financial transactions. Key words: Bank Cheques, CNN, OCR, Line Sweeping.

The handwritten signature is perhaps the most accustomed way for the acknowledgement of the consent of an individual or the authentication of the identity of a person in numerous transactions. In addition, the authenticity of a questioned offline or static handwritten signature still poses a case of interest, especially in forensic related applications. A common approach in offline signature verification system is to apply several predetermined image analysis models. Consequently, any offline signature sample which originates from either authentic persons or forgers, utilizes a fixed feature extraction base. In this proposed study, the feature space and the corresponding projection values depend on the training samples only; thus the proposed method can be found useful in forensic cases. In order to do so, we reenter a groundbreaking unsupervised learning method named archetypal analysis, which is connected to effective data analysis approaches such as sparse coding. Due to the fact that until recently there was no efficient implementation publicly available, archetypal analysis had only few cases of use. However, a fast optimization scheme using an active set strategy is now available. The main goal of this work is to introduce archetypal analysis for offline signature verification. The output of the archetypal analysis of few reference samples is a set of archetypes which are used to form the base of the feature space. Then, given a set of archetypes and a signature sample under examination archetypal analysis and average pooling provides the corresponding features. The promising performance of the proposed approach is demonstrated with the use of an evaluation method which employs the popular CEDAR and MCYT75 signature datasets.

The typical features of the coordinate and the curvature as well as the recorded time information were analyzed in the hand-written signatures. In the hand-written signature process 10 biometric features were summarized: the amount of zero speed in direction x and direction y, the amount of zero acceleration in direction x and direction y, the total time of the hand-written signatures, the total distance of the pen traveling in the hand-written process, the frequency for lifting the pen, the time for lifting the pen, the amount of the pressure higher or lower than the threshold values. The formulae of biometric features extraction were summarized. The Gauss function was used to draw the typical information from the above-mentioned biometric features, with which to establish the hidden Markov mode and to train it. The frame of double authentication was proposed by combing the signature with the digital signature. Web service technology was applied in the system to ensure the security of data transmission. The training practice indicates that the hand-written signature verification can satisfy the needs from the office automation systems.

Although handwritten signature verification has been extensively researched, it has not achieved an optimal classification accuracy rate. Therefore, efficient and accurate signature verification techniques are required since signatures are still widely used as a means of personal verification. This research work presents efficient distance-based classification techniques as an alternative to supervised learning classification techniques (SLTs). The Local Directional Pattern (LDP) feature extraction technique was used to analyze the effect of using several different distance-based classification techniques. The classification techniques tested, are the Euclidean, Manhattan, Fractional, weighted Euclidean, weighted Manhattan, weighted fractional distances and individually optimized resampling of feature vector sizes. The best accuracy, of 90.8%, was achieved through applying a combination of the weighted fractional distances and locally optimized resampling classification techniques to the Local Directional Pattern feature extraction. These results are compared with results from literature, where the same feature extraction technique was classified with SLTs. The distance-based classification was found to produce greater accuracy than the SLTs.

This paper presents one zone-based feature extraction approach for online handwritten signature recognition and verification of one of the major Indic scripts–Devanagari. To the best of our knowledge no work is available for signature recognition and verification in Indic scripts. Here, the entire online image is divided into a number of local zones. In this approach, named Zone wise Slopes of Dominant Points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are supplied to two different classifiers; Hidden Markov Model (HMM) and Support Vector Machine (SVM) for recognition and verification of signatures. An exhaustive experiment in a large dataset is performed using this zone-based feature on original and forged signatures in Devanagari script and encouraging results are found.