Python OCR- Learn Optical Character Recognition from Scratch

Python OCR: Learn Optical Character Recognition from Scratch, available at $19.99, has an average rating of 4.38, with 27 lectures, based on 4 reviews, and has 1020 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginner to intermediate level programmers interested in learning OCR with Python or Students or professionals in computer science, data science, and related fields or Programmers interested in implementing OCR in their projects or Researchers or professionals working with document analysis or data entry tasks or Anyone interested in understanding the fundamentals and practical applications of optical character recognition. It is particularly useful for Beginner to intermediate level programmers interested in learning OCR with Python or Students or professionals in computer science, data science, and related fields or Programmers interested in implementing OCR in their projects or Researchers or professionals working with document analysis or data entry tasks or Anyone interested in understanding the fundamentals and practical applications of optical character recognition.

Enroll now: Python OCR: Learn Optical Character Recognition from Scratch

Summary

Title: Python OCR: Learn Optical Character Recognition from Scratch

Price: $19.99

Average Rating: 4.38

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: ?799

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

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!

Course Curriculum

Chapter 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?

Chapter 2: Model Development and Prediction

Lecture 1: Download Dataset

Lecture 2: Python Code

Lecture 3: Pre-trained Model

Lecture 4: Prediction Folder

Lecture 5: Enabling GPU in Google Colab

Lecture 6: Current Status of GPU

Lecture 7: Connect Google Colab with Google Drive

Lecture 8: Import Libraries

Lecture 9: Pre-Process the Data

Lecture 10: Splitting Pre-Processed Image Data

Lecture 11: Displaying a Random Image

Lecture 12: Define Model

Lecture 13: Printing Model Summary

Lecture 14: Visualise the Model Architecture

Lecture 15: Callback

Lecture 16: Model Training

Lecture 17: Loading Pre-Trained Weights

Lecture 18: Prediction

Lecture 19: Performance Evaluation

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

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2 stars: 1 votes

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5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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