Handwritten Signature Verification for Mobile Phones

Paudel, Nilakantha; Querini, Marco; Italiano, Giuseppe F.

doi:10.5220/0005675200460052

Cited by 6 publications

(6 citation statements)

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“…Through our medical prescription features, we obtained better accuracy; however, additional features can be extracted from original data as described in [ 41 ], certain features have been extracted from the handwritten medical prescription application, and the accuracy has been calculated. The proposed features are discussed: Pen length (PL): the path length is the total length covered by the user's pen tip throughout the signature creation Pen diagonal length (DL): diagonal length is the maximum ( x max, y max) and minimum ( x min, y min) points in the X - Y coordinate Time length (TL): the total time of writing the complete signature (the time period between the first pen down and last pen up) Mean speed (MS): mean speed is the average speed and velocity of the user writing the signature Covariance X - Y (CXY): covariance means to measure the scattered points on the signature path Vector length ratio (VLR): calculate all vector points of the signatures from the beginning to each x - y coordinate …”

Section: Resultsmentioning

confidence: 99%

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Recognition of Handwritten Medical Prescription Using Signature Verification Techniques

Rani

Rehman

Yousaf

et al. 2022

Computational and Mathematical Methods in Medicine

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Patient record keeping plays a vital role in diagnoses and cures. Due to a shortage of time, most doctors write prescriptions manually in Pakistan. At times, it becomes difficult for pharmacists to read prescriptions properly. As a result, they may dispense the wrong medicine. This might cause risky and deadly effects on the patient's health. This paper proposes an online handwritten medical prescription recognition system that lets doctors write prescriptions on a tablet using a stylus and automatically recognizes the medicine. We use signature verification techniques to recognize the doctor's handwriting to overcome the problem of misinterpretation of the medicine name by the pharmacist. The proposed system stores different features like the pen coordinates, time, and several pen-ups and pen-downs. Besides using features already proposed in the literature for signature verification, we propose some new features that greatly enhance recognition accuracy. We built a dataset of 24 medicine names from two users and compared results using newly proposed features. We have obtained 84%, 78%, 77.47% 77.31%, 74.17%, 60%, 38.5%, 68%, and 61.64% accuracies for 9 users using SVM classifier.

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Section: Resultsmentioning

confidence: 99%

“…The comparison of both types of features obtained from user 1 and user 9 is shown in Figures 8 and 9 , which infers that the SVM is not proven as a better choice for signature verification features [ 41 ].…”

Section: Resultsmentioning

confidence: 99%

Recognition of Handwritten Medical Prescription Using Signature Verification Techniques

Rani

Rehman

Yousaf

et al. 2022

Computational and Mathematical Methods in Medicine

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“…As mentioned above, verification of finger drawn signatures currently receives great attention but is still a challenging issue due to the lack of precise signals and features. N. Sae-Bae et al [11], M. Antal et al [12], and N. Paudel et al [13] propose their own matching algorithms, not the machine learning techniques, for finger drawn signature verification. A. Buriro [3] applies deep learning algorithm for finger drawn signature verification using dynamic features such as finger movements and phone movements.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed scheme applies mean square error (MSE) for the similarity comparison. MSE refers to the difference between S and S and is defined as Equation (13). Using MSE, the verification rule is defined as Equation (14).…”

Section: L(s S ) = ∑ |S − S | (12)mentioning

confidence: 99%

“…Early studies [4][5][6][7][8][9][10] primarily focused on verifying signatures drawn by a stylus. Finger-drawn signature verification [3,[11][12][13] is relatively not well researched yet; however, it is currently actively discussed because most smartphones do not have a stylus, and finger signing is more convenient. The verification of finger-drawn signatures involves new challenges because it results in less precise signals than signatures drawn using a stylus.…”

Section: Introductionmentioning

confidence: 99%

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Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

et al. 2018

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This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN) for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector), which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE) as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

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