Python Ocr: Learn Optical Character Recognition From Scratch
Python Ocr: Learn Optical Character Recognition From Scratch
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 268.55 MB | Duration: 0h 55m
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.
Enhance problem-solving skills and ability to apply machine learning concepts in real-world situations.
Requirements
Basic knowledge of Python programming language
Description
Are you interested in computer vision and optical character recognition (OCR)? Do you want to learn how to build powerful OCR systems using Python and deep learning frameworks such as Keras and TensorFlow? Look no further than our comprehensive course on OCR using Python!In this course, you will learn the fundamentals of OCR and computer vision, including image preprocessing, feature extraction, and model training. You will gain hands-on experience building an OCR system from scratch using Python and deep learning, and learn how to use popular libraries such as OpenCV to preprocess images and extract features.Our course also includes a complete project where you will develop a CAPTCHA recognition OCR system, allowing you to put your skills into practice and build a real-world application. With this project, you will learn how to approach complex OCR problems and develop solutions that meet the needs of modern applications.Not only will you gain a solid understanding of OCR and computer vision, but you will also acquire valuable skills that are in high demand in the job market. Upon completion of this course, you will have the skills and knowledge to develop advanced OCR systems and build applications that solve real-world problems. Don't miss out on this opportunity to enhance your skills and open up new career opportunities!
Overview
Section 1: Fundamentals
Lecture 1 Introduction
Lecture 2 What is Optical Character Recognition (OCR)?
Lecture 3 Optical Character Recognition Applications
Lecture 4 Traditional OCR Vs. Deep learning OCR
Lecture 5 About this project
Lecture 6 Why Python and Keras?
Lecture 7 Why Google Colab?
Lecture 8 How CAPTCHA Recognition is Done?
Section 2: Model Development and Prediction
Lecture 9 Download Dataset
Lecture 10 Python Code
Lecture 11 Pre-trained Model
Lecture 12 Prediction Folder
Lecture 13 Enabling GPU in Google Colab
Lecture 14 Current Status of GPU
Lecture 15 Connect Google Colab with Google Drive
Lecture 16 Import Libraries
Lecture 17 Pre-Process the Data
Lecture 18 Splitting Pre-Processed Image Data
Lecture 19 Displaying a Random Image
Lecture 20 Define Model
Lecture 21 Printing Model Summary
Lecture 22 Visualise the Model Architecture
Lecture 23 Callback
Lecture 24 Model Training
Lecture 25 Loading Pre-Trained Weights
Lecture 26 Prediction
Lecture 27 Performance Evaluation
Beginner to intermediate level programmers interested in learning OCR with Python,Students or professionals in computer science, data science, and related fields,Programmers interested in implementing OCR in their projects,Researchers or professionals working with document analysis or data entry tasks,Anyone interested in understanding the fundamentals and practical applications of optical character recognition
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