| 1 - Introduction.mp4 | 18.9 MB | ||
| 10 - Python Code.mp4 | 2.4 MB | ||
| 10 - code.ipynb | 600.3 KB | ||
| 11 - Pretrained Model.mp4 | 3.8 MB | ||
| 11 - ocr-weights.h5 | 20.9 MB | ||
| 12 - Prediction Folder.mp4 | 3.4 MB | ||
| 12 - prediction.zip | 42.5 KB | ||
| 13 - Enabling GPU in Google Colab.mp4 | 5.3 MB | ||
| 14 - Current Status of GPU.mp4 | 7 MB | ||
| 15 - Connect Google Colab with Google Drive.mp4 | 5.2 MB | ||
| 16 - Import Libraries.mp4 | 16.1 MB | ||
| 17 - PreProcess the Data.mp4 | 15.4 MB | ||
| 18 - Splitting PreProcessed Image Data.mp4 | 12.3 MB | ||
| 19 - Displaying a Random Image.mp4 | 11.5 MB | ||
| 2 - What is Optical Character Recognition OCR.mp4 | 6.5 MB | ||
| 20 - Define Model.mp4 | 20.6 MB | ||
| 21 - Printing Model Summary.mp4 | 7.5 MB | ||
| 22 - Visualise the Model Architecture.mp4 | 2.8 MB | ||
| 23 - Callback.mp4 | 6.8 MB | ||
| 24 - Model Training.mp4 | 6.4 MB | ||
| 25 - Loading PreTrained Weights.mp4 | 3.1 MB | ||
| 26 - Prediction.mp4 | 12.7 MB | ||
| 27 - Performance Evaluation.mp4 | 7.3 MB | ||
| 3 - Optical Character Recognition Applications.mp4 | 23.5 MB | ||
| 4 - Traditional OCR Vs Deep learning OCR.mp4 | 10.4 MB | ||
| 5 - About this project.mp4 | 4.1 MB | ||
| 6 - Why Python and Keras.mp4 | 4.9 MB | ||
| 7 - Why Google Colab.mp4 | 8.7 MB | ||
| 8 - How CAPTCHA Recognition is Done.mp4 | 7 MB | ||
| 9 - Download Dataset.mp4 | 4.7 MB | ||
| 9 - dataset.zip | 8.7 MB | ||
| [Tutorialsplanet.NET].url | 102.4 B | ||
| ▲ 33 total files | |||
Udemy - Python OCR: Learn Optical Character Recognition from Scratch [TP]
https://iwebp.extraimage.xyz/webp/300:250/90/CJEW4LsOK5MjRukb_p_ok3T3ag77TJaDA1pcToko9C5BHdqtVLCEUAdKWzMZavzs07d1Ls.webp
Optical Character Recognition with Python: Build Your Own OCR System using Keras, Tensorflow, and Computer Vision
What you'll learn
Understand the basics of Optical Character Recognition (OCR) technology and its applications.
Learn how to preprocess and prepare data for OCR model training using Python and OpenCV.
Gain an understanding of deep learning concepts, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), & their application to OCR
Develop hands-on experience in building and training OCR models using Keras, a deep learning library in Python.
Learn how to evaluate OCR models and measure their performance using metrics such as accuracy and loss.
Understand how to apply OCR models to real-world problems, such as captcha recognition.
Develop an appreciation for the potential of OCR technology and its impact on various industries, including healthcare, finance, and legal.
For more Udemy Courses: https://tutorialsplanet.net
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