ujjawaltyagii / Advance-ANPR-FRS

Kavach'23 - Team: 404_Found

Advanced ANPR and FRS

Kavach'23 - Team: 404_Found
P.S. Code : KVH-005

Phase 1 : Testing normal plate recogintion

• Successfully tested normal character recognition using python scripts and OpenCV

Is that all ?

• Nope it all starts here

1. we have also prepared the ground work for Easy OCR and TensorFlow Object Detection
2. We are done setting up Virtual env/Kernel for our ANPR system (so while integrating Face Recognition System there won't be any problem about over-writing of libraries)

Steps in creating Virtual environment

Step - 1 : python -m venv anprsys

Step - 2 : Activate your virtual environment
source tfod/bin/activate # Linux
.\arpysns\Scripts\activate # Windows

Step - 3 : Install dependencies and add virtual environment to the Python Kernel
python -m pip install --upgrade pip
pip install ipykernel
python -m ipykernel install --user --name=anprsys

3. We are also in middle of completion of integrating TensorFlow API for out project

4. To train model we will be using Transfer learning method in initial phase

Upcoming Plan

!. Initially we will be using kaggel dataset and manually divided the collected images into two folders train and test so that all the images and annotations will be split among these two folders.

2. We will be using pre-trained state-of-the-art model and just fine tuned it on our particular specific use case. We will begin the training process by opening Real Time Number Plate Detection and installed the Tensoflow Object Detection (TFOD) + API

Tech Stach :

1. Opencv
2. Easy OCR
3. Tensorflow
4. Jupyter Notebook/Jupyter Lab
5. Python Libraries (more can be used depending upon the implementation*)

Mentor : Ms Vernika Singh

Team Members :

1. Surya Pratap Singh Chauhan
2. Ujjawal Tyagi
3. Yash Vardhan Singh
4. Shravya Vashishtha
5. Suryansh Kapil
6. Shreya Srivastava