Automatic Number Plate Recognition, OCR Web App in Python

图像处理和对象检测是数据科学的领域之一，在当今世界的行业中具有广泛的应用。许多行业正在寻找具有这些技能的数据科学家。本课程涵盖建模技术，包括标记对象检测数据（图像），数据预处理，深度学习模型构建（InceptionResNet V2），评估和生产（Web App）

我们将开始本课程“项目体系结构”，然后使用Python开发此应用。然后，我将展示如何使用图像注释工具（它是在python GUI（pyQT）中开发的开源软件）来收集数据和标签图像，以用于车牌或车牌的对象检测。

然后，在标记图像后，我们将在TensorFlow 2中进行数据预处理，构建和训练深度学习对象检测模型（InceptionResnet V2）。一旦以最佳损失训练了模型，我们将对模型进行评估。

在最后一个模块中，我们将学习使用FLASK Python创建一个Web应用程序项目。最初，我们将学习Flask中的基本概念，例如URL路由，渲染模板，模板继承等。然后，我们将使用HTML，Bootstrap创建网站。这样，我们终于可以使用我们的应用程序了。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 41 lectures (3h 8m) | Size: 1.93 GB
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

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