getcontrol / KYC-train-model

Train TensorFlow model for KYC

Train Tensorflow Model for Tensorflow Verification

This repo contains the Python code to produce the dataset and train the Tensorflow model for use in KYC-tensorflow. This training model transfers learning from a previously trained model mobilenet_v2.

Installation Instructions

1. Clone the repo.

git clone https://github.com/getcontrol/KYC-train-model

cd KYC-train-model

2. Download and unzip the the MIDV-500 formatted dataset to 'verification-train-model' directory.

https://www.dropbox.com/s/dmjbat0e1re5rkf/midv_500.zip?dl=0

3. Make a 'data' directory in verification-train-model for the dataset generation and an 'output' folder for testing.

mkdir data

mkdir output

4. Create and activate a Python 3 Virtual environment.

python3 -m venv env

source env/bin/activate

5. Install Requirements.

pip install -r requirements.txt

6. Synthesize training data.

python synthesis_data.py

python receipt_dataset.py

7. Train model.

python train.py

Test Model

Samples are included in 'test_samples_600*800'.

python test.py

Citation

Please cite this paper, if using midv dataset, link for dataset provided in paper

@article{DBLP:journals/corr/abs-1807-05786,
  author    = {Vladimir V. Arlazarov and
               Konstantin Bulatov and
               Timofey S. Chernov and
               Vladimir L. Arlazarov},
  title     = {{MIDV-500:} {A} Dataset for Identity Documents Analysis and Recognition
               on Mobile Devices in Video Stream},
  journal   = {CoRR},
  volume    = {abs/1807.05786},
  year      = {2018},
  url       = {http://arxiv.org/abs/1807.05786},
  archivePrefix = {arXiv},
  eprint    = {1807.05786},
  timestamp = {Mon, 13 Aug 2018 16:46:35 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1807-05786},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}