Deprecated - Switched to Pytorch
ML_IDCard_Segmentation (Tensorflow / Keras)
Machine Learning Project to identify an ID Card on an image.
Objectives
The goal of this project is to recognize a ID Card on a photo, cut it out using semantic segmentation and to transform the perspective so that you get a front view of the ID Card. Optionally an OCR text recognition can be done in a later step. However, this is not yet planned in the scope of this project.
Additional Information
Dataset: MIDV-500
Tensorflow Version: GPU 1.5.0
Installation
- Create and activate a new environment.
conda create -n idcard python=3.6
source activate idcard
- Install Dependencies.
pip install -r requirements.txt
Download and Prepare Dataset
Download the image files (image and ground_truth).
Splits the data into training, test and validation data.
python prepare_dataset.py
Training of the neural network
python train.py
Show Jupyter Notebook for Test
jupyter notebook "IDCard Prediction Test.ipynb"
Test the trained model
python test.py test/sample1.png --output_mask=test/output_mask.png --output_prediction=test/output_pred.png --model=model.h5
Call python test.py --help for possible arguments.
Additional commands
Starts Tensorboard Visualisation.
tensorboard --logdir=logs/
Background Information
Model
A U-NET was used as the model. U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany. The network is based on the fully convolutional networkand its architecture was modified and extended to work with fewer training images and to yield more precise segmentations. Segmentation of a 512*512 image takes less than a second on a modern GPU.
Metrics
The Metric IoU (Intersection over Unit / Jaccard-Coefficient) was used
to measure the quality of the model.
The closer the Jaccard coefficient is to 1, the greater the similarity of the quantities. The minimum value of the Jaccard coefficient is 0.
Example:
Results for validation set (only trained on german id cards)
Accuracy:
99.87%
Intersection over Unit:
0.9939
Pipeline Example:
