Udemy - Automatic Number Plate Recognition, OCR Web App in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.1 GB
  • Uploaded Bytutsnode
  • Downloads369
  • Last checkedMay. 09th '21
  • Date uploadedMay. 06th '21
  • Seeders 46
  • Leechers13
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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

Requirements

Basic knowledge on Python
Knowledge on Deep learning with TensorFlow
Basics on HTML

Last Updated 3/2021

Files:

Automatic Number Plate Recognition, OCR Web App in Python [TutsNode.com] - Automatic Number Plate Recognition, OCR Web App in Python 1. Introduction
  • 2.1 Project_Files.zip (473.4 MB)
  • 2. Download the Resources.html (0.1 KB)
  • 1. Project Architecture.srt (3.4 KB)
  • 1. Project Architecture.mp4 (12.5 MB)
8. Number Plate Web App
  • 6. Integrate Deep Learning Object Detection Model.srt (15.3 KB)
  • 8. Display Output in HTML Page.srt (9.5 KB)
  • 5. HTTP Method Upload File in Flask.srt (8.6 KB)
  • 9. Display Output in HTML Page part 2.srt (7.4 KB)
  • 6. Integrate Deep Learning Object Detection Model.mp4 (141.7 MB)
  • 7. Integrate Number Plate Detection and OCR to Flask App.srt (6.1 KB)
  • 4. Upload Form in HTML.srt (3.8 KB)
  • 1. Create Web App.srt (3.8 KB)
  • 3. Template Inheritance.srt (3.3 KB)
  • 2. Footer.srt (2.2 KB)
  • 8. Display Output in HTML Page.mp4 (78.2 MB)
  • 9. Display Output in HTML Page part 2.mp4 (71.2 MB)
  • 7. Integrate Number Plate Detection and OCR to Flask App.mp4 (66.9 MB)
  • 5. HTTP Method Upload File in Flask.mp4 (56.7 MB)
  • 1. Create Web App.mp4 (25.7 MB)
  • 4. Upload Form in HTML.mp4 (22.8 MB)
  • 3. Template Inheritance.mp4 (22.2 MB)
  • 2. Footer.mp4 (12.8 MB)
5. Pipeline Object Detection Model
  • 1. Make Predictions.srt (10.8 KB)
  • 5. Create Pipeline.srt (5.7 KB)
  • 4. Bounding Box.srt (5.4 KB)
  • 2. Make Predictions part2.srt (4.9 KB)
  • 3. De-normalize the Output.srt (4.1 KB)
  • 1. Make Predictions.mp4 (74.9 MB)
  • 5. Create Pipeline.mp4 (55.4 MB)
  • 4. Bounding Box.mp4 (39.1 MB)
  • 3. De-normalize the Output.mp4 (30.6 MB)
  • 2. Make Predictions part2.mp4 (30.0 MB)
3. Data Processing
  • 3. Data Preprocessing.srt (10.6 KB)
  • 1. Read Data.srt (8.2 KB)
  • 2. Verify Labeled Data.srt (6.7 KB)
  • 4. Split train and test set.srt (4.0 KB)
  • 3. Data Preprocessing.mp4 (83.4 MB)
  • 1. Read Data.mp4 (61.1 MB)
  • 2. Verify Labeled Data.mp4 (48.6 MB)
  • 4. Split train and test set.mp4 (27.4 MB)
2. Labeling
  • 1. Get the Data.srt (1.2 KB)
  • 2. Download Image Annotation Tool.srt (1.7 KB)
  • 3. Install Dependencies.srt (1.2 KB)
  • 4. Label Images.srt (1.9 KB)
  • 5. XML to CSV.srt (6.6 KB)
  • 5. XML to CSV.mp4 (81.9 MB)
  • 3. Install Dependencies.mp4 (40.3 MB)
  • 4. Label Images.mp4 (32.1 MB)
  • 2. Download Image Annotation Tool.mp4 (22.8 MB)
  • 1. Get the Data.mp4 (18.6 MB)
  • 2.1 labelImg-master.zip (6.3 MB)
7. Flask App
  • 3. Render HTML Template.srt (7.9 KB)
  • 2. First Flask App.srt (6.5 KB)
  • 1. Install Visual Studio Code.srt (4.6 KB)
  • 4. Import Boostrap.srt (3.2 KB)
  • 3. Render HTML Template.mp4 (47.6 MB)
  • 1. Install Visual Studio Code.mp4 (38.8 MB)
  • 2. First Flask App.mp4 (38.2 MB)
  • 4. Import Boostrap.mp4 (25.7 MB)
4. Deep Learning for Object Detection
  • 2. InceptionResnet V2 model building.srt (7.2 KB)
  • 8. Tensorboard.srt (4.8 KB)
  • 3. Defining Inputs and Outputs.srt (1.7 KB)
  • 4. Compiling Model.srt (2.7 KB)
  • 6. InceptionResnet V2 Training - Part 2.srt (2.7 KB)
  • 5. InceptionResnet V2 Training.srt (3.8 KB)
  • 7. Save Deep Learning Model.srt (2.7 KB)
  • 1. Get Transfer Learning from TensorFlow 2.x.srt (3.1 KB)
  • 2. InceptionResnet V2 model building.mp4 (45.0 MB)
  • 8. Tensorboard.mp4 (28.2 MB)
  • 6. InceptionResnet V2 Training - Part 2.mp4 (24.6 MB)
  • 7. Save Deep Learning Model.mp4 (24.1 MB)
  • 4. Compiling Model.mp4 (23.9 MB)
  • 5. InceptionResnet V2 Training.mp4 (21.5 MB)
  • 1. Get Transfer Learning from TensorFlow 2.x.mp4 (17.4 MB)
  • 3. Defining Inputs and Outputs.mp4 (14.4 MB)
6. Optical Character Recognition (OCR)
  • 3. Exrtract Number Plate text from Image.srt (7.1 KB)
  • 1. Install Tesseract.srt (5.0 KB)
  • 2. Install Pytesseract.srt (1.7 KB)
  • 3. Exrtract Number Plate text from Image.mp4 (67.4 MB)
  • 1. Install Tesseract.mp4 (47.8 MB)
  • 2. Install Pytesseract.mp4 (13.0 MB)
9. BONUS
  • 1. Bonus Lecture.html (0.7 KB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
    • .pad
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