The document presents a CNN-based approach for handwritten signature recognition, emphasizing its importance in verifying legal documents to combat forgery. It discusses the methodology, which includes data preprocessing and the use of the GoogLeNet model for classification and feature extraction. The study outlines the experimental setup, data specifications, and mentions the training and validation process, highlighting the critical nature of signature validation.

1. CNN based Handwritten
Signature Recognition
Presented By:
Suresh Pokharel, IOE Pulchowk Campus
Santosh Giri, Kathford Int’l College of Engg. and Mgmt.
Prof. Dr. Subarna Shakya, IOE Pulchowk Campus

2. Outline
❏ Background
❏ Motivation
❏ Methodology
❏ Results
❏ Summary
❏ Limitations
❏ Future Enhancement
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3. Background
● Signature is commonly accepted as a means of verifying
the legality of documents such as certificates, checks,
drafts, letters, approvals etc.
● Problems in countering the forgery and falsification of
such documents in diverse financial, legal, academic, and
other commercial settings.
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4. Background
❏ Signature verification task is very critical and often
presents difficulties like high variability
(Factors: Age, behavior and environment, similarities
between signatures of different person and similarity in
duplication or forgery of one’s signature.)
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5. Background
Types of Signature Verification Techniques:
❏ Online Verification
Consists of electronic signing system that uses dynamic
data features such as the speed, pressure, pen’s position,
altitude angle etc.
❏ Offline Verification
Takes use of static features of two dimensional image
pixel. 5

6. Motivation
● Validation of signature in legal documents are highly critical.
● Failure in the authentication may lead to serious
consequences and damages.
● New and complex forgery and fraud techniques are
emerging.
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7. Methodology
Data collection and Preprocessing:
❏ Collected in hard copy
❏ Converted into an image scanner
❏ Image Preprocessing Techniques:
cropping, scaling (224px X 224px)
❏ Image Size: 40 KiloBytes (Approx.)
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9. Methodology (Contd...)
❏ The pre-trained CNN model, GoogleNet is used for experiment and the
tensorflow platform is used.
❏ GoogleNet model consists of two parts; a classification layer and a feature
extraction layer.
❏ The parameters on the classification layer are removed and trained with the
transfer values from the feature extraction layer of the model.
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10. Experiment Setup
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Primary Data Specification:
❏ Classes: 25
❏ Samples per class: 100
❏ Training set: 65%
❏ Validation: 20%
❏ Testing: 15%
Hardware
❏ DELL: Intel i5, 1.7 GHZ
processor
❏ 7.7GiB Memory

11. Results
Training and Validation Accuracy graph.
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12. Results
Training Results:
Training and Validation Accuracy graph.
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13. Thank You
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