YOLO: Automatic License Plate Detection & Extract text App
YOLO: Automatic License Plate Detection & Extract text App
Duration: 5h 18m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 4.02 GB
Genre: eLearning | Language: English
Duration: 5h 18m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 4.02 GB
Genre: eLearning | Language: English
Learn to Develop License Plate Object Detection, OCR and Create Web App Project using Deep Learning, TensorFlow 2, Flask
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
Requirements:
Basic knowledge on Python
Knowledge on Deep learning with TensorFlow
Basics on HTML
Description:
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.
Who this course is for:
Anyone who want to build deep learning project from sctrach
A python developer who want to develop Number Plate OCR Project
Anyone who want to learn end to end Deep Learning Project
Who are curious in developing Web App project in TensorFlow 2
More Info