Inspiration
My grandfather was an expert in handwriting analysis. He spent all his life analyzing documents for the CBI (Central Bureau Of Investigation) and other organizations. His unique way of analyzing documents using a magnifying glass and different tools required huge amounts of time and patience to analyze a single document. This is back when computers were not fast enough. I remember vividly that he photocopied the same document multiple times and arranged it on the table to gain a closer look at the handwriting style.
What it does
SigNet (Detecting Signature Similarity Using Machine Learning/Deep Learning): Is This the End of Human Forensic Analysis?
How I built it
How: To build our Signature Similarity network, we will use utilize the wonders of Deep Learning. We will go through three approaches to extract the similarity between our handwritten signatures. For our initial data, we will use the HandWritten Signatures dataset from Kaggle.
Requirements For this project we will require: Python 3.8: The Programming Language TensorFlow 2: The Deep Learning Library Numpy: Linear Algebra Matplotlib: Plotting images Scikit-Learn: General Machine Learning Library
Challenges I ran into
Lack of data
Accomplishments that I'm proud of
Reasonable/acceptable accuracy and metrics
What I learned
Data Science, Data Wrangling, A.I
What's next for SigNet (Signature Detection Network)
To build this into a commercial product.
Built With
- keras
- numpy
- opencv
- pandas
- python
- scikit-learn
- seaborn
- tensorflow
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