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  Authors

Enba Prabhu M,Dr. R. N. Devendra Kumar,Gokulraj G,Jasswanth M,Dharani Dharan A

  Keywords

Convolutional neural network (CNN); Signature verification; Pre-processing, Pre-trained Model, Fine-tuning, Classification.

  Abstract


Signature Verification Is An Important Aspect Of Contemporary Security Systems, As It Verifies The Authenticity Of A Person Through His Or Her Own Handwritten Signature. As More And More Systems Rely On Digital Systems, There Is A Greater Demand For Automated, Precise, And Effective Signature Verification Systems. Conventional Signature Verification Techniques Include Manual Verification Or Simple Automated Methods, Which Are Time-Consuming And Susceptible To Errors. This Paper Introduces A Novel Signature Verification Method With Convolutional Neural Networks (Cnns) That Utilize Deep Learning Methods To Examine And Validate Signatures. Through The Combination Of A CNN-Based Model With An Easy-To-Use Streamlit Frontend, This System Provides An End-User-Friendly Experience For Real-Time Verification Of Handwritten Signatures. The New Approach Is To Preprocess The Input Signature Images Through Normalization Of Their Sizes, Conversion Into Grayscale, And Improvement Of Their Quality For Uniformity. The Preprocessed Signature Images Are Then Input Into A CNN Model That Extracts The Hierarchical Features, Including Edges, Textures, And Shapes, Automatically, Through A Sequence Of Convolutional And Pooling Layers. This Allows The Model To Extract Efficiently The Subtle Differences Between Authentic And Fake Signatures. The CNN Model Is Trained On A Labeled Dataset Of Authentic And Forged Signatures Using Backpropagation To Adjust The Weights And Enhance The Accuracy Of The Model. The CNN Model Architecture Involves Several Convolutional Layers Followed By Fully Connected Layers, Which Forms A Strong Foundation For Classification. The Model Is Then Deployed Into A Streamlit-Based Frontend Where Users Can Upload Signature Images To Be Verified. Streamlit Offers An Interactive And Light-Weight Environment, Allowing Users To See The Outcome Of The Signature Verification In Real-Time. The System Provides A Yes Or No Output On Whether The Uploaded Signature Is Genuine Or Fake Depending On The Predictions Of The CNN Model. The Streamlit Interface Also Offers A Friendly Presentation Of The Process, Displaying The Uploaded Signature, The Analysis Of The CNN, And The Final Verification Outcome. This Method Has Great Benefits Compared To The Conventional Methods, Such As Higher Accuracy, Scalability, And Capability To Process Large Datasets. The Application Of CNNs In Signature Verification Guarantees High-Level Feature Extraction And Reduces Human Error. Furthermore, The Application Of Streamlit Makes The System Easier To Use And More Accessible, Making It Applicable In Various Fields, Ranging From Banking To Legal Systems. The Integration Of CNN-Based Signature Authentication And Streamlit Frontend Is An Advanced Solution To This Important Security Problem.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2505271

  Paper ID - 284873

  Author type - Indian Author

  Page Number(s) - c387-c393

  Pubished in - Volume 13 | Issue 5 | May 2025

  DOI (Digital Object Identifier) -   

  No Of Downloads - 99

  Author Country - India, 641010, Coimbatore, Coimbatore, 641010, Science and Technology

  Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Published Paper PDF : - http://www.ijcrt.org/papers/IJCRT2505271

  Published Paper URL: : - http://ijcrt.org/viewfull.php?&p_id=IJCRT2505271

  Published Paper PDF Downlaod: - download.php?file=IJCRT2505271

  Cite this article

Enba Prabhu M,Dr. R. N. Devendra Kumar,Gokulraj G,Jasswanth M,Dharani Dharan A,   "Signature Verification using Convolutional Neural Network", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 5, pp.c387-c393, May 2025, Available at :http://www.ijcrt.org/papers/IJCRT2505271.pdf

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