Learn to Develop License Plate Object Detection, OCR and Create Web App Project using Deep Learning, TensorFlow 2, Flask
Welcome to NUMBER PLATE DETECTION AND OCR: A DEEP LEARNING WEB APP PROJECT from scratch
What you’ll learn
- Object Detection from Scratch.
- License Plate Detection.
- Extract text from Image using Tesseract.
- Train InceptionResnet V2 in TensorFlow 2 for Object Detection.
- Flask Based Web API.
- Labeling Object Detection Data using Image Annotation Tool.
- Train custom YOLO model from scratch.
- Real time license plate detection with YOLO.
Course Content
- Introduction –> 6 lectures • 19min.
- Labeling –> 5 lectures • 19min.
- Data Processing –> 4 lectures • 27min.
- Deep Learning for Object Detection –> 9 lectures • 25min.
- Pipeline Object Detection Model –> 5 lectures • 27min.
- Optical Character Recognition (OCR) –> 5 lectures • 27min.
- Flask App –> 4 lectures • 20min.
- Number Plate Web App –> 10 lectures • 53min.
- Real Time Number Plate Recognition with YOLO –> 18 lectures • 1hr 40min.
- BONUS –> 1 lecture • 1min.
Requirements
- Basic knowledge on Python.
- Knowledge on Deep learning with TensorFlow.
- Basics on HTML.
Welcome to NUMBER PLATE DETECTION AND OCR: A DEEP LEARNING WEB APP PROJECT from scratch
Image Processing and Object Detection is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers modeling techniques including labeling Object Detection data (images), data preprocessing, Deep Learning Model building (InceptionResNet V2), evaluation, and production (Web App)
We start this course Project Architecture that was followed to Develop this App in Python. Then I will show how to gather data and label images for object detection for Licence Plate or Number Plate using Image Annotation Tool which is open-source software developed in python GUI (pyQT).
Then after we label the image we will work on data preprocessing, build and train deep learning object detection model (InceptionResnet V2) in TensorFlow 2. Once the model is trained with the best loss, we will evaluate the model. I will show you how to calculate the
- Intersection Over Union (IoU)
- The precision of the object detection model.
Once we have done with the Object Detection model, then using this model we will crop the image which contains the license plate which is also called the region of interest (ROI), and pass the ROI to Optical Character Recognition API Tesseract in Python (Pytesseract). In this model, I will show you how to extract text from images. Now, we will put it all together and build a Pipeline Deep Learning model.
In the final module, we will learn to create a web app project using FLASK Python. Initially, we will learn basics concepts in Flask like URL routing, render the template, template inheritance, etc. Then we will create our website using HTML, Bootstrap. With that we are finally ready with our App.
WHAT YOU WILL LEARN?
- Building Project in Python Programming
- Labeling Image for Object Detection
- Train Object Detection model (InceptionResNet V2) in TensorFlow 2.x
- Model Evaluation
- Optical Character Recognition with Pytesseract
- Flask API
- Flask Web App Development in HTML, Boostrap, Python
We know that Computer Vision-Based Web App is one of those topics that always leaves some doubts. Feel free to ask questions in Q & A and we are very happy to answer all your questions.
We also provided all Notebooks, py files in the resources which will useful for reference.