Signature Verification App for doodleblue
Signature Verification App for doodleblue
A person’s signature is representative of his identity. For us at the Bank, a signed document by a customer is an instruction from him for carrying out an approved transaction for him.
On on-boarding a customer we capture an image of his signature in our systems, and on receiving a signed document (Cheques, DDs, and others) from him we match the signature on the document with the one recorded in the database before proceeding with the instruction.
In the case of skilled forgeries, it becomes very difficult to verify the identity of the customer.
We want you to build a system that can help us distinguish forgeries from actual signatures. This system should be able to study signature parameters as strokes, curves, dots, dashes, writing fluidity & style, in a Writer-Independent manner and create features for identification of the signature.
The system should not use any existing APIs and should be completely self-developed.
How should it work?
The system shall work in 2 steps:
Step 1: Accept & Store Genuine Signature Image: Take actual signature scanned image of the onboarding customer and store it in a database against a unique Customer ID
Step 2: Accept & Compare Signature Images: Accept inputs of Customer ID and corresponding signature image. Compare with the signature stored in DB against the given Customer ID, and return a Confidence Match Score between the two signature images.
Demo Video Youtube
Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
git clone https://github.com/koushik-elite/Signature-Verification.gitcd Signature-Verification
UI and Backend code with pretrained model is available in
.├── application│ ├── model│ │ ├── model.pt│ │ └── model_v4.pt│ ├── server│ │ ├── data│ │ │ ├── data (copy).csv│ │ │ ├── data.csv│ │ │ └── uploads│ │ │ ├── compare│ │ │ └── original│ │ ├── dataset.py│ │ ├── model.py│ │ ├── persist.py│ │ ├── __pycache__│ │ │ ├── dataset.cpython-36.pyc│ │ │ ├── model.cpython-36.pyc│ │ │ └── persist.cpython-36.pyc│ │ └── server.py│ └── signatureVerify│ ├── angular.json│ ├── browserslist│ ├── e2e│ │ ├── protractor.conf.js│ │ ├── src│ │ │ ├── app.e2e-spec.ts│ │ │ └── app.po.ts│ │ └── tsconfig.json│ ├── karma.conf.js│ ├── package.json│ ├── package-lock.json│ ├── README.md│ ├── src│ │ ├── app│ │ │ ├── add-signature│ │ │ │ ├── add-signature.component.css│ │ │ │ ├── add-signature.component.html│ │ │ │ ├── add-signature.component.spec.ts│ │ │ │ ├── add-signature.component.ts│ │ │ │ └── image-preview.directive.ts│ │ │ ├── app.component.css│ │ │ ├── app.component.html│ │ │ ├── app.component.spec.ts│ │ │ ├── app.component.ts│ │ │ ├── app.module.ts│ │ │ ├── app-routing.module.ts│ │ │ ├── compare-signature│ │ │ │ ├── compare-signature.component.css│ │ │ │ ├── compare-signature.component.html│ │ │ │ └── compare-signature.component.ts│ │ │ └── services│ │ │ └── application.service.ts│ │ ├── assets│ │ ├── environments│ │ │ ├── environment.prod.ts│ │ │ └── environment.ts│ │ ├── favicon.ico│ │ ├── index.html│ │ ├── main.ts│ │ ├── polyfills.ts│ │ ├── styles.css│ │ └── test.ts│ ├── tsconfig.app.json│ ├── tsconfig.json│ ├── tsconfig.spec.json│ └── tslint.json├── assets│ ├── flow.png│ ├── loss.jpg│ └── network-architecture.jpg├── dataset.py├── installation│ └── README.md├── LICENSE├── loss.py├── model├── model.py├── prepare.py├── README.md├── requirements.txt├── sample_Signature│ ├── forged│ │ └── NFI-08805004.png│ ├── genuine│ │ ├── NFI-00101001.png│ ├── README.txt.txt│ └── train_final.csv├── Signature Training.ipynb├── Signature Training kaggle.ipynb└── testfiles├── 049│ ├── 01_049.png│ ├── 02_049.png│ ├── 03_049.png│ ├── 04_049.png│ ├── 05_049.png│ ├── 06_049.png│ ├── 07_049.png│ ├── 08_049.png│ ├── 09_049.png│ ├── 10_049.png│ ├── 11_049.png│ └── 12_049.png├── 049_forg│ ├── 01_0114049.PNG│ ├── 01_0206049.PNG│ ├── 01_0210049.PNG│ ├── 02_0114049.PNG│ ├── 02_0206049.PNG│ ├── 02_0210049.PNG│ ├── 03_0114049.PNG│ ├── 03_0206049.PNG│ ├── 03_0210049.PNG│ ├── 04_0114049.PNG│ ├── 04_0206049.PNG│ └── 04_0210049.PNG├── forg│ └── NFI-00301001.png└── gen└── NFI-00101001.png
Download the pretrained deep learning model from the google drive. since github is limited to 100mb only
Download Model.zip
Extract the model to
API is in python and UI is in angular 6 so both the servers need to be started saperately.
Start Angular Server
got to
install Node and NPM (both stable version)
run on command-prompt/shell
npm installng serve
Start Python Flask Server
got to
Create a environment and install python flask, follow installation document
run on command-prompt/shell
python server.py
This problem comes under One Shot Classification problem where 2 images will be compared directly and find similarity between them. these kind of clasification are mainly used to compare Human Faces, Signature etc.
Siamese networks are a special type of neural network architecture. Instead of a model learning to classify its inputs, the neural networks learns to differentiate between two inputs. It learns the similarity between them.
Here i defined a custom loss function which is used to calculate the euclidean distance between 2 output from the model
Y is either 1 or 0. If the inputs are from the same class , then the value of Y is 0 , otherwise Y is 1
i used Softmax on the difference of 2 outputs and generate probability of Y, Sum of probability is 1
The training is done in the jupyter notepad Signature Training.ipynb using the sample signature images given with the task sample task.zip in email
download and extract the “sample_Signature” folder in the same notebook folder
Folder Path
run the prepare.py python code for data preparation
A CSV file “train_final.csv” will be created in “sample_Signature” folder
Run the jupyter notebook Signature Training.ipynb for training.
model_v4 in the
Use the notebook Signature Training kaggle.ipynb upload it to kaggle and use this dataset signature-verification-dataset for training download the model and use it for production
One Shot Learning with Siamese Networks in PyTorch hackernoon
Siamese Neural Networks for One-shot Image Recognition PDF