Signature Verification System to Automatically Decide Whether a Scanned Signature Image is Genuine or Forged

Abstract

This paper introduces a strong system for checking handwritten signatures without needing to see the person sign in real time. This system helps stop fraud in important areas like banking and legal documents. The process starts with preparing the signature image by turning it into grayscale, removing noise, making it black and white, and adjusting its size. Then, it uses two methods, Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), to extract important features from the signature. A Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel is used to classify the signature as real or fake. This classifier is trained using a process called grid-search cross-validation. The system is built using Python 3. 9 along with libraries like OpenCV, scikit-learn, and NumPy. It also includes a simple user interface made with Flask or Tkinter. When tested on a standard dataset like GPDS, the system achieved an overall accuracy of 96. 5%, a false-acceptance rate of 2. 3%, and a false-rejection rate of 3. 7%. It can process each signature quickly, under 200 milliseconds, and performs well compared to other advanced methods.

Introduction

Handwritten signatures are still commonly used for identity verification in legal, financial, and administrative documents. However, modern technology has made forgery easier, reducing trust in signatures. Human verification is still common but is slow, costly, and has an error rate of 5–10%.

2. Problem and Solution

The system addresses the challenge of verifying scanned handwritten signatures using machine learning and image processing. It:

• Achieves ≥96% accuracy

• Maintains FAR ≤ 3%, FRR ≤ 4%

• Delivers results in <200 ms

• Works on standard devices using regular flatbed scanners

3. Key System Features

Input & Constraints

• Input: Grayscale image (300 dpi), resized to 256×256

• System must run in <200 ms on a standard CPU

• Total model size must be <50 MB for deployment on low-resource devices

4. System Architecture

Pipeline Components:

1. Data Acquisition

   • Uses GPDS dataset: 24 real and 30 forged signatures per user

   • Images stored in PNG for clarity

2. Preprocessing

   • Grayscale conversion

   • Noise removal (median filtering)

   • Binarization (Otsu’s method)

   • Normalization and centering

3. Feature Extraction

   • HOG (Histogram of Oriented Gradients): Captures stroke direction

   • LBP (Local Binary Patterns): Captures texture

   • Combined into a single feature vector (~9,859 dimensions)

4. Classification

   • SVM with RBF kernel

   • Hyperparameters tuned via 5-fold cross-validation

   • Model saved in svm_rbf.pkl (under 50 MB)

5. User Interface

   • Built with Flask or Tkinter

   • Real-time results: Genuine or Forged with confidence

   • Includes signature template management and storage (SQLite)

5. Algorithm Workflow

1. Preprocessing (Grayscale → Noise removal → Binarization → Resize)

2. Feature Extraction

   • HOG (stroke direction)

   • LBP (texture patterns)

3. Classification

   • SVM predicts genuineness

   • Threshold set at Equal Error Rate (EER) for balanced accuracy

6. Implementation Details

• Languages & Libraries: Python 3.9, OpenCV, scikit-learn, NumPy, scikit-image, Flask/Tkinter

• Folders:

  • data/: raw and preprocessed images

  • features/: stored .npy files

  • models/: trained classifier and normalization data

  • src/: modular Python code

• Code Examples:

  • Feature extraction using hog() and local_binary_pattern()

  • SVM training via GridSearchCV

7. Results & Performance

• Accuracy ≥96%

• Low error rates (FAR ≤ 3%, FRR ≤ 4%)

• <200 ms processing time per signature

• Compact model suitable for low-resource environments

Conclusion

Deep learning. This paper shows a full offline signature verification system that works well with accuracy, speed, and ease of use. It uses traditional image processing and machine learning methods. The system includes a strong preprocessing step, uses two types of feature extraction (HOG and LBP), and uses an improved SVM classifier. Testing on the GPDS dataset shows that the system is accurate with 96. 5% overall accuracy. It has a 2. 3% false acceptance rate and a 3. 7% false rejection rate. The equal error rate is 3. 0%, showing a good balance in how it makes decisions. The system runs in 180 milliseconds on regular hardware, which is fast enough for real-time use. The model uses less than 50 MB of memory, so it works well on point-of-sale machines and desktop computers. Even though it has good qualities, it has some limitations. It uses only still images of signatures and depends on a dataset that\'s not too big. It doesn\'t include information about how the signature is made, like the order of strokes or the path the pen takes. This makes it harder to catch fake signatures that copy how someone writes over time. Also, it has some issues when dealing with very unique signatures or when the signing conditions change, like different scanners or lighting. In the future, we plan to improve this in four main ways: 1) Adding online signature data: Using synthesized stroke features or tablet data to better understand how signatures are made, which can help in making better decisions. 2) Using better deep learning models: Trying lightweight neural networks, like MobileNet, to find more useful features without using too much time or memory. 3) Making it work on mobile devices: Moving the system to phones and tablets, using tools like TensorFlow Lite to do the verification directly on the device. 4) Expanding the dataset: Collecting and testing the system on bigger and more varied sets of signatures so it works well across different styles, languages, and types of devices. By working on these areas, we want to make the system even more accurate, useful in more situations, and combine the best parts of old and new methods for signature verification

References

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Copyright

Copyright © 2025 Prof. Girish Patil, Hrishikesh Gavane, Samarth Kadam, Yash Kale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.