Automatic Number Plate Recognition, OCR Web App in Python [04.2021, ENG]
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Files
| 1. Introduction | |||
| 1. Project Architecture.mp4 | 12.49 MB | ||
| 1. Project Architecture.srt | 3.36 KB | ||
| 2. Download the Resources.html | 113 B | ||
| 2.1 Project_Files.zip | 473.38 MB | ||
| 2. Labeling | |||
| 1. Get the Data.mp4 | 18.58 MB | ||
| 1. Get the Data.srt | 1.18 KB | ||
| 2. Download Image Annotation Tool.mp4 | 22.78 MB | ||
| 2. Download Image Annotation Tool.srt | 1.66 KB | ||
| 2.1 labelImg-master.zip | 6.28 MB | ||
| 3. Install Dependencies.mp4 | 40.33 MB | ||
| 3. Install Dependencies.srt | 1.18 KB | ||
| 4. Label Images.mp4 | 32.08 MB | ||
| 4. Label Images.srt | 1.9 KB | ||
| 5. XML to CSV.mp4 | 81.86 MB | ||
| 5. XML to CSV.srt | 6.62 KB | ||
| 3. Data Processing | |||
| 1. Read Data.mp4 | 61.14 MB | ||
| 1. Read Data.srt | 8.16 KB | ||
| 2. Verify Labeled Data.mp4 | 48.62 MB | ||
| 2. Verify Labeled Data.srt | 6.67 KB | ||
| 3. Data Preprocessing.mp4 | 83.36 MB | ||
| 3. Data Preprocessing.srt | 10.61 KB | ||
| 4. Split train and test set.mp4 | 27.4 MB | ||
| 4. Split train and test set.srt | 3.96 KB | ||
| 4. Deep Learning for Object Detection | |||
| 1. Get Transfer Learning from TensorFlow 2.x.mp4 | 17.43 MB | ||
| 1. Get Transfer Learning from TensorFlow 2.x.srt | 3.07 KB | ||
| 2. InceptionResnet V2 model building.mp4 | 45 MB | ||
| 2. InceptionResnet V2 model building.srt | 7.2 KB | ||
| 3. Defining Inputs and Outputs.mp4 | 14.45 MB | ||
| 3. Defining Inputs and Outputs.srt | 1.69 KB | ||
| 4. Compiling Model.mp4 | 23.94 MB | ||
| 4. Compiling Model.srt | 2.67 KB | ||
| 5. InceptionResnet V2 Training.mp4 | 21.48 MB | ||
| 5. InceptionResnet V2 Training.srt | 3.77 KB | ||
| 6. InceptionResnet V2 Training - Part 2.mp4 | 24.6 MB | ||
| 6. InceptionResnet V2 Training - Part 2.srt | 2.66 KB | ||
| 7. Save Deep Learning Model.mp4 | 24.07 MB | ||
| 7. Save Deep Learning Model.srt | 2.67 KB | ||
| 8. Tensorboard.mp4 | 28.23 MB | ||
| 8. Tensorboard.srt | 4.78 KB | ||
| 5. Pipeline Object Detection Model | |||
| 1. Make Predictions.mp4 | 74.93 MB | ||
| 1. Make Predictions.srt | 10.81 KB | ||
| 2. Make Predictions part2.mp4 | 30.03 MB | ||
| 2. Make Predictions part2.srt | 4.85 KB | ||
| 3. De-normalize the Output.mp4 | 30.59 MB | ||
| 3. De-normalize the Output.srt | 4.06 KB | ||
| 4. Bounding Box.mp4 | 39.08 MB | ||
| 4. Bounding Box.srt | 5.42 KB | ||
| 5. Create Pipeline.mp4 | 55.4 MB | ||
| 5. Create Pipeline.srt | 5.72 KB | ||
| 6. Optical Character Recognition (OCR) | |||
| 1. Install Tesseract.mp4 | 47.8 MB | ||
| 1. Install Tesseract.srt | 4.98 KB | ||
| 2. Install Pytesseract.mp4 | 12.98 MB | ||
| 2. Install Pytesseract.srt | 1.73 KB | ||
| 3. Exrtract Number Plate text from Image.mp4 | 67.37 MB | ||
| 3. Exrtract Number Plate text from Image.srt | 7.09 KB | ||
| 7. Flask App | |||
| 1. Install Visual Studio Code.mp4 | 38.79 MB | ||
| 1. Install Visual Studio Code.srt | 4.61 KB | ||
| 2. First Flask App.mp4 | 38.2 MB | ||
| 2. First Flask App.srt | 6.45 KB | ||
| 3. Render HTML Template.mp4 | 47.65 MB | ||
| 3. Render HTML Template.srt | 7.94 KB | ||
| 4. Import Boostrap.mp4 | 25.69 MB | ||
| 4. Import Boostrap.srt | 3.22 KB | ||
| 8. Number Plate Web App | |||
| 1. Create Web App.mp4 | 25.71 MB | ||
| 1. Create Web App.srt | 3.77 KB | ||
| 2. Footer.mp4 | 12.76 MB | ||
| 2. Footer.srt | 2.23 KB | ||
| 3. Template Inheritance.mp4 | 22.21 MB | ||
| 3. Template Inheritance.srt | 3.33 KB | ||
| 4. Upload Form in HTML.mp4 | 22.79 MB | ||
| 4. Upload Form in HTML.srt | 3.84 KB | ||
| 5. HTTP Method Upload File in Flask.mp4 | 56.66 MB | ||
| 5. HTTP Method Upload File in Flask.srt | 8.55 KB | ||
| 6. Integrate Deep Learning Object Detection Model.mp4 | 141.72 MB | ||
| 6. Integrate Deep Learning Object Detection Model.srt | 15.33 KB | ||
| 7. Integrate Number Plate Detection and OCR to Flask App.mp4 | 66.89 MB | ||
| 7. Integrate Number Plate Detection and OCR to Flask App.srt | 6.09 KB | ||
| 8. Display Output in HTML Page.mp4 | 78.17 MB | ||
| 8. Display Output in HTML Page.srt | 9.46 KB | ||
| 9. Display Output in HTML Page part 2.mp4 | 71.25 MB | ||
| 9. Display Output in HTML Page part 2.srt | 7.35 KB | ||
| 9. BONUS | |||
| 1. Bonus Lecture.html | 685 B |
Description
Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Программирование (видеоуроки)
Automatic Number Plate Recognition, OCR Web App in Python
Год выпуска: 04.2021
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/deep-learning-web-app-project-number-plate-detection-ocr/
Автор: Data Science Anywhere and Srikanth Gusksra
Продолжительность: 3 hours 8 min
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: Automatic Number Plate Recognition, OCR Web App in Python, is a training course on model plate number recognition, OCR and building web projects using deep learning, Flow Tensor 2 and Flask framework. Image processing and object recognition is one of the sub-topics of data science, which includes a wide range of applications in industry in the world. Many companies are looking for data science specialists who have these skills. This course covers modeling technologies including data tagging of detected objects, data processing, building deep learning models, evaluation and web application production. In this course, you will learn how to build a project in the Python programming language, tag images for object recognition, develop object recognition models, model evaluation, use Pytesseract for OCR, and work with the Flask API.
What you will learn in the course Automatic Number Plate Recognition, OCR Web App in Python:
Distinguish objects from the base
Identify car license plate numbers
Extract texts from images using Tesseract
Learn InceptionResnet V2 in Flow Tensor 2 to detect objects
Flask based web API
Формат видео: MP4
Видео: AVC, 1280x720, 16:9, 30fps, 3000kbps
Аудио: АAC, 2 ch, 128 Kbps
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