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

UdemyAutomaticNumberPlateRecognitionPython2021

种子大小：2.06 GB

收录时间：2025-06-03

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文件列表：82File

  1. 1. Introduction/2.1 Project_Files.zip473.38 MB
  2. 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.mp4141.72 MB
  3. 3. Data Processing/3. Data Preprocessing.mp483.36 MB
  4. 2. Labeling/5. XML to CSV.mp481.86 MB
  5. 8. Number Plate Web App/8. Display Output in HTML Page.mp478.17 MB
  6. 5. Pipeline Object Detection Model/1. Make Predictions.mp474.93 MB
  7. 8. Number Plate Web App/9. Display Output in HTML Page part 2.mp471.25 MB
  8. 6. Optical Character Recognition (OCR)/3. Exrtract Number Plate text from Image.mp467.37 MB
  9. 8. Number Plate Web App/7. Integrate Number Plate Detection and OCR to Flask App.mp466.89 MB
  10. 3. Data Processing/1. Read Data.mp461.14 MB
  11. 8. Number Plate Web App/5. HTTP Method Upload File in Flask.mp456.66 MB
  12. 5. Pipeline Object Detection Model/5. Create Pipeline.mp455.4 MB
  13. 3. Data Processing/2. Verify Labeled Data.mp448.62 MB
  14. 6. Optical Character Recognition (OCR)/1. Install Tesseract.mp447.8 MB
  15. 7. Flask App/3. Render HTML Template.mp447.65 MB
  16. 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.mp445 MB
  17. 2. Labeling/3. Install Dependencies.mp440.33 MB
  18. 5. Pipeline Object Detection Model/4. Bounding Box.mp439.08 MB
  19. 7. Flask App/1. Install Visual Studio Code.mp438.79 MB
  20. 7. Flask App/2. First Flask App.mp438.2 MB
  21. 2. Labeling/4. Label Images.mp432.08 MB
  22. 5. Pipeline Object Detection Model/3. De-normalize the Output.mp430.59 MB
  23. 5. Pipeline Object Detection Model/2. Make Predictions part2.mp430.03 MB
  24. 4. Deep Learning for Object Detection/8. Tensorboard.mp428.23 MB
  25. 3. Data Processing/4. Split train and test set.mp427.4 MB
  26. 8. Number Plate Web App/1. Create Web App.mp425.71 MB
  27. 7. Flask App/4. Import Boostrap.mp425.69 MB
  28. 4. Deep Learning for Object Detection/6. InceptionResnet V2 Training - Part 2.mp424.6 MB
  29. 4. Deep Learning for Object Detection/7. Save Deep Learning Model.mp424.07 MB
  30. 4. Deep Learning for Object Detection/4. Compiling Model.mp423.94 MB
  31. 8. Number Plate Web App/4. Upload Form in HTML.mp422.79 MB
  32. 2. Labeling/2. Download Image Annotation Tool.mp422.78 MB
  33. 8. Number Plate Web App/3. Template Inheritance.mp422.21 MB
  34. 4. Deep Learning for Object Detection/5. InceptionResnet V2 Training.mp421.48 MB
  35. 2. Labeling/1. Get the Data.mp418.58 MB
  36. 4. Deep Learning for Object Detection/1. Get Transfer Learning from TensorFlow 2.x.mp417.43 MB
  37. 4. Deep Learning for Object Detection/3. Defining Inputs and Outputs.mp414.45 MB
  38. 6. Optical Character Recognition (OCR)/2. Install Pytesseract.mp412.98 MB
  39. 8. Number Plate Web App/2. Footer.mp412.76 MB
  40. 1. Introduction/1. Project Architecture.mp412.49 MB
  41. 2. Labeling/2.1 labelImg-master.zip6.28 MB
  42. 8. Number Plate Web App/6. Integrate Deep Learning Object Detection Model.srt15.33 KB
  43. 5. Pipeline Object Detection Model/1. Make Predictions.srt10.81 KB
  44. 3. Data Processing/3. Data Preprocessing.srt10.61 KB
  45. 8. Number Plate Web App/8. Display Output in HTML Page.srt9.46 KB
  46. 8. Number Plate Web App/5. HTTP Method Upload File in Flask.srt8.55 KB
  47. 3. Data Processing/1. Read Data.srt8.16 KB
  48. 7. Flask App/3. Render HTML Template.srt7.94 KB
  49. 8. Number Plate Web App/9. Display Output in HTML Page part 2.srt7.35 KB
  50. 4. Deep Learning for Object Detection/2. InceptionResnet V2 model building.srt7.2 KB
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