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

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Udemy - Automatic Number Plate Recognition, OCR Web App in Python (Size: 2.1 GB)
  0 614.4 B
  1. Bonus Lecture.html 716.8 B
  1. Create Web App.mp4 25.7 MB
  1. Create Web App.srt 3.8 KB
  1. Get Transfer Learning from TensorFlow 2.x.mp4 17.4 MB
  1. Get Transfer Learning from TensorFlow 2.x.srt 3.1 KB
  1. Get the Data.mp4 18.6 MB
  1. Get the Data.srt 1.2 KB
  1. Install Tesseract.mp4 47.8 MB
  1. Install Tesseract.srt 5 KB
  1 206.1 KB
  1. Install Visual Studio Code.mp4 38.8 MB
  1. Install Visual Studio Code.srt 4.6 KB
  1. Make Predictions.mp4 74.9 MB
  1. Make Predictions.srt 10.8 KB
  1. Project Architecture.mp4 12.5 MB
  1. Project Architecture.srt 3.4 KB
  1. Read Data.mp4 61.1 MB
  1. Read Data.srt 8.2 KB
  2. Download Image Annotation Tool.mp4 22.8 MB
  2. Download Image Annotation Tool.srt 1.7 KB
  2. Download the Resources.html 102.4 B
  2. First Flask App.mp4 38.2 MB
  2. First Flask App.srt 6.5 KB
  2. Footer.mp4 12.8 MB
  2. Footer.srt 2.2 KB
  2. InceptionResnet V2 model building.mp4 45 MB
  2. InceptionResnet V2 model building.srt 7.2 KB
  2. Install Pytesseract.mp4 13 MB
  2. Install Pytesseract.srt 1.7 KB
  2. Make Predictions part2.mp4 30 MB
  2. Make Predictions part2.srt 4.9 KB
  2. Verify Labeled Data.mp4 48.6 MB
  2 139.1 KB
  2. Verify Labeled Data.srt 6.7 KB
  2.1 Project_Files.zip 473.4 MB
  2.1 labelImg-master.zip 6.3 MB
  3. Data Preprocessing.mp4 83.4 MB
  3. Data Preprocessing.srt 10.6 KB
  3. De-normalize the Output.mp4 30.6 MB
  3. Exrtract Number Plate text from Image.srt 7.1 KB
  3 141 KB
  3. De-normalize the Output.srt 4.1 KB
  3. Defining Inputs and Outputs.mp4 14.4 MB
  3. Defining Inputs and Outputs.srt 1.7 KB
  3. Exrtract Number Plate text from Image.mp4 67.4 MB
  3. Install Dependencies.mp4 40.3 MB
  3. Install Dependencies.srt 1.2 KB
  3. Render HTML Template.mp4 47.6 MB
  3. Render HTML Template.srt 7.9 KB
  3. Template Inheritance.mp4 22.2 MB
  3. Template Inheritance.srt 3.3 KB
  4. Bounding Box.mp4 39.1 MB
  4. Bounding Box.srt 5.4 KB
  4. Compiling Model.mp4 23.9 MB
  4 339.1 KB
  4. Compiling Model.srt 2.7 KB
  4. Import Boostrap.mp4 25.7 MB
  4. Import Boostrap.srt 3.2 KB
  4. Label Images.mp4 32.1 MB
  4. Label Images.srt 1.9 KB
  4. Split train and test set.mp4 27.4 MB
  4. Split train and test set.srt 4 KB
  4. Upload Form in HTML.mp4 22.8 MB
  4. Upload Form in HTML.srt 3.8 KB
  5. Create Pipeline.mp4 55.4 MB
  5. Create Pipeline.srt 5.7 KB
  5. HTTP Method Upload File in Flask.mp4 56.7 MB
  5. HTTP Method Upload File in Flask.srt 8.6 KB
  5. InceptionResnet V2 Training.mp4 21.5 MB
  5. XML to CSV.srt 6.6 KB
  5 73.6 KB
  5. InceptionResnet V2 Training.srt 3.8 KB
  5. XML to CSV.mp4 81.9 MB
  6. Integrate Deep Learning Object Detection Model.mp4 141.7 MB
  6. Integrate Deep Learning Object Detection Model.srt 15.3 KB
  TutsNode.com.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
  6 257.7 KB
  6. InceptionResnet V2 Training - Part 2.mp4 24.6 MB
  6. InceptionResnet V2 Training - Part 2.srt 2.7 KB
  7 137.2 KB
  7. Integrate Number Plate Detection and OCR to Flask App.mp4 66.9 MB
  7. Integrate Number Plate Detection and OCR to Flask App.srt 6.1 KB
  7. Save Deep Learning Model.mp4 24.1 MB
  7. Save Deep Learning Model.srt 2.7 KB
  8. Display Output in HTML Page.mp4 78.2 MB
  8 113.7 KB
  8. Display Output in HTML Page.srt 9.5 KB
  8. Tensorboard.mp4 28.2 MB
  8. Tensorboard.srt 4.8 KB
  9. Display Output in HTML Page part 2.mp4 71.2 MB
  9. Display Output in HTML Page part 2.srt 7.4 KB
  9 369 KB
  10 350.4 KB
  11 105.1 KB
  12 386.9 KB
  13 202.5 KB
  14 359.1 KB
  15 510.7 KB
  16 177.6 KB
  17 431 KB
  18 212.7 KB
  19 302.5 KB
  20 433.4 KB
  21 415.3 KB
  22 484 KB
  23 274.6 KB
  24 103.3 KB
  25 302 KB
  26 320.8 KB
  27 413 KB
  28 440.2 KB
  29 57.9 KB
  30 217.5 KB
  31 225.5 KB
  32 294.6 KB
  33 15.4 KB
  34 425.3 KB
  35 69.3 KB
  36 54.7 KB
  37 22.8 KB
  38 245.7 KB
  39 11.2 KB
  ▲ 124 total files

Description


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

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