[Udemy] Automatic Number Plate Recognition, OCR Web App in Python (04.2021)

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[Udemy] Automatic Number Plate Recognition, OCR Web App in Python (04.2021)

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文件大小： 2.06G
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收录时间：2022-01-10
最近下载：2025-07-31

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文件列表

  • 1. Introduction/2.1 Project_Files.zip 496.4 MB
  • 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.mp4 148.6 MB
  • 3. Data Processing/3. Data Preprocessing.mp4 87.4 MB
  • 2. Labeling/5. XML to CSV.mp4 85.8 MB
  • 8. Number Plate Web App/8. Display Output in HTML Page.mp4 82.0 MB
  • 5. Pipeline Object Detection Model/1. Make Predictions.mp4 78.6 MB
  • 8. Number Plate Web App/9. Display Output in HTML Page part 2.mp4 74.7 MB
  • 6. Optical Character Recognition (OCR)/3. Exrtract Number Plate text from Image.mp4 70.6 MB
  • 8. Number Plate Web App/7. Integrate Number Plate Detection and OCR to Flask App.mp4 70.1 MB
  • 3. Data Processing/1. Read Data.mp4 64.1 MB
  • 8. Number Plate Web App/5. HTTP Method Upload File in Flask.mp4 59.4 MB
  • 5. Pipeline Object Detection Model/5. Create Pipeline.mp4 58.1 MB
  • 3. Data Processing/2. Verify Labeled Data.mp4 51.0 MB
  • 6. Optical Character Recognition (OCR)/1. Install Tesseract.mp4 50.1 MB
  • 7. Flask App/3. Render HTML Template.mp4 50.0 MB
  • 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.mp4 47.2 MB
  • 2. Labeling/3. Install Dependencies.mp4 42.3 MB
  • 5. Pipeline Object Detection Model/4. Bounding Box.mp4 41.0 MB
  • 7. Flask App/1. Install Visual Studio Code.mp4 40.7 MB
  • 7. Flask App/2. First Flask App.mp4 40.1 MB
  • 2. Labeling/4. Label Images.mp4 33.6 MB
  • 5. Pipeline Object Detection Model/3. De-normalize the Output.mp4 32.1 MB
  • 5. Pipeline Object Detection Model/2. Make Predictions part2.mp4 31.5 MB
  • 4. Deep Learning for Object Detection/8. Tensorboard.mp4 29.6 MB
  • 3. Data Processing/4. Split train and test set.mp4 28.7 MB
  • 8. Number Plate Web App/1. Create Web App.mp4 27.0 MB
  • 7. Flask App/4. Import Boostrap.mp4 26.9 MB
  • 4. Deep Learning for Object Detection/6. InceptionResnet V2 Training - Part 2.mp4 25.8 MB
  • 4. Deep Learning for Object Detection/7. Save Deep Learning Model.mp4 25.2 MB
  • 4. Deep Learning for Object Detection/4. Compiling Model.mp4 25.1 MB
  • 8. Number Plate Web App/4. Upload Form in HTML.mp4 23.9 MB
  • 2. Labeling/2. Download Image Annotation Tool.mp4 23.9 MB
  • 8. Number Plate Web App/3. Template Inheritance.mp4 23.3 MB
  • 4. Deep Learning for Object Detection/5. InceptionResnet V2 Training.mp4 22.5 MB
  • 2. Labeling/1. Get the Data.mp4 19.5 MB
  • 4. Deep Learning for Object Detection/1. Get Transfer Learning from TensorFlow 2.x.mp4 18.3 MB
  • 4. Deep Learning for Object Detection/3. Defining Inputs and Outputs.mp4 15.1 MB
  • 6. Optical Character Recognition (OCR)/2. Install Pytesseract.mp4 13.6 MB
  • 8. Number Plate Web App/2. Footer.mp4 13.4 MB
  • 1. Introduction/1. Project Architecture.mp4 13.1 MB
  • 2. Labeling/2.1 labelImg-master.zip 6.6 MB
  • 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.srt 15.7 kB
  • 5. Pipeline Object Detection Model/1. Make Predictions.srt 11.1 kB
  • 3. Data Processing/3. Data Preprocessing.srt 10.9 kB
  • 8. Number Plate Web App/8. Display Output in HTML Page.srt 9.7 kB
  • 8. Number Plate Web App/5. HTTP Method Upload File in Flask.srt 8.8 kB
  • 3. Data Processing/1. Read Data.srt 8.4 kB
  • 7. Flask App/3. Render HTML Template.srt 8.1 kB
  • 8. Number Plate Web App/9. Display Output in HTML Page part 2.srt 7.5 kB
  • 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.srt 7.4 kB
  • 6. Optical Character Recognition (OCR)/3. Exrtract Number Plate text from Image.srt 7.3 kB
  • 3. Data Processing/2. Verify Labeled Data.srt 6.8 kB
  • 2. Labeling/5. XML to CSV.srt 6.8 kB
  • 7. Flask App/2. First Flask App.srt 6.6 kB
  • 8. Number Plate Web App/7. Integrate Number Plate Detection and OCR to Flask App.srt 6.2 kB
  • 5. Pipeline Object Detection Model/5. Create Pipeline.srt 5.9 kB
  • 5. Pipeline Object Detection Model/4. Bounding Box.srt 5.6 kB
  • 6. Optical Character Recognition (OCR)/1. Install Tesseract.srt 5.1 kB
  • 5. Pipeline Object Detection Model/2. Make Predictions part2.srt 5.0 kB
  • 4. Deep Learning for Object Detection/8. Tensorboard.srt 4.9 kB
  • 7. Flask App/1. Install Visual Studio Code.srt 4.7 kB
  • 5. Pipeline Object Detection Model/3. De-normalize the Output.srt 4.2 kB
  • 3. Data Processing/4. Split train and test set.srt 4.1 kB
  • 8. Number Plate Web App/4. Upload Form in HTML.srt 3.9 kB
  • 4. Deep Learning for Object Detection/5. InceptionResnet V2 Training.srt 3.9 kB
  • 8. Number Plate Web App/1. Create Web App.srt 3.9 kB
  • 1. Introduction/1. Project Architecture.srt 3.4 kB
  • 8. Number Plate Web App/3. Template Inheritance.srt 3.4 kB
  • 7. Flask App/4. Import Boostrap.srt 3.3 kB
  • 4. Deep Learning for Object Detection/1. Get Transfer Learning from TensorFlow 2.x.srt 3.1 kB
  • 4. Deep Learning for Object Detection/7. Save Deep Learning Model.srt 2.7 kB
  • 4. Deep Learning for Object Detection/4. Compiling Model.srt 2.7 kB
  • 4. Deep Learning for Object Detection/6. InceptionResnet V2 Training - Part 2.srt 2.7 kB
  • 8. Number Plate Web App/2. Footer.srt 2.3 kB
  • 2. Labeling/4. Label Images.srt 1.9 kB
  • 6. Optical Character Recognition (OCR)/2. Install Pytesseract.srt 1.8 kB
  • 4. Deep Learning for Object Detection/3. Defining Inputs and Outputs.srt 1.7 kB
  • 2. Labeling/2. Download Image Annotation Tool.srt 1.7 kB
  • 2. Labeling/1. Get the Data.srt 1.2 kB
  • 2. Labeling/3. Install Dependencies.srt 1.2 kB
  • 9. BONUS/1. Bonus Lecture.html 685 Bytes
  • 1. Introduction/2. Download the Resources.html 113 Bytes

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