101 points by sign_verifier_creator 1 year ago flag hide 15 comments
user2 1 year ago next
Can this be used to detect forged signatures as well?
user1 1 year ago next
That's a great question! The system could potentially be adapted to detect forgeries but it would require more data and training. Right now, it's mainly focused on verifying authentic signatures.
user1 1 year ago prev next
This is really cool! I've been working on something similar and this is some impressive work.
user3 1 year ago prev next
What type of deep learning algorithms were used?
user1 1 year ago next
A convolutional neural network (CNN) was used architecture. The model was trained with binary cross entropy as the loss function, with the Adam optimizer.
user4 1 year ago prev next
What kind of input data was used in training the model?
user1 1 year ago next
The model was trained using a few thousand scanned signature images. The signatures came from a variety of people and signature styles. The images were preprocessed and normalized to ensure consistent input data.
user5 1 year ago prev next
What kind of testing or evaluation metrics were used to validate the system's accuracy and performance?
user1 1 year ago next
The model's performance was evaluated with standard classification evaluation metrics, like accuracy, precision, recall, and F1 score. The model achieved over 95% accuracy on the test set. Confusion matrices, ROC curves, and other classification metrics were also used for evaluation.
user6 1 year ago prev next
How does this work in terms of real-world use cases? Is there any available public API?
user1 1 year ago next
Currently, there isn't a public API available for this project. However, it could definitely be integrated into real-world use cases. Imagine implementing this in an e-signature platform to prevent fraud. Or, it could be used in banks or other businesses that routinely require handwritten signatures.
user7 1 year ago prev next
I have some concerns with the generalizability of the system. Wouldn't subtle differences in scanners, printers, or paper types affect the verification process?
user1 1 year ago next
That's a valid concern. The variability that comes with different scanner or printer qualities definitely creates challenges. In order to account for this, the dataset should be diversified to include many different scanning/printing conditions. In order to further mitigate this issue, additional data augmentation methods could be implemented during preprocessing.
user8 1 year ago prev next
I think this can be a breakthrough in the field of forensic science. Ever imagined, a CSI might use this in the near future? :D
user9 1 year ago prev next
Very elegant solution! I'm really impressed with how straightforward the code is as well. Great job!