Automatic Number Plate Recognition, OCR Web App in Python

Automatic Number Plate Recognition, OCR Web App in Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3 Hours | 2.06 GB

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

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
Table of Contents

Introduction
1 Project Architecture
2 Download the Resources

Labeling
3 Get the Data
4 Download Image Annotation Tool
5 Install Dependencies
6 Label Images
7 XML to CSV

Data Processing
8 Read Data
9 Verify Labeled Data
10 Data Preprocessing
11 Split train and test set

Deep Learning for Object Detection
12 Get Transfer Learning from TensorFlow 2.x
13 InceptionResnet V2 model building
14 Defining Inputs and Outputs
15 Compiling Model
16 InceptionResnet V2 Training
17 InceptionResnet V2 Training – Part 2
18 Save Deep Learning Model
19 Tensorboard

Pipeline Object Detection Model
20 Make Predictions
21 Make Predictions part2
22 De-normalize the Output
23 Bounding Box
24 Create Pipeline

Optical Character Recognition (OCR)
25 Install Tesseract
26 Install Pytesseract
27 Exrtract Number Plate text from Image

Flask App
28 Install Visual Studio Code
29 First Flask App
30 Render HTML Template
31 Import Boostrap

Number Plate Web App
32 Create Web App
33 Footer
34 Template Inheritance
35 Upload Form in HTML
36 HTTP Method Upload File in Flask
37 Integrate Deep Learning Object Detection Model
38 Integrate Number Plate Detection and OCR to Flask App
39 Display Output in HTML Page
40 Display Output in HTML Page part 2

BONUS
41 Bonus Lecture

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