About This Course
Welcome to the third course from my Computer Vision series – Python Optical Character Recognition And Object Detection.
Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition (OCR) are among the most used applications of Computer Vision.
These techniques enable computers to recognize and classify either a whole image or multiple objects inside a single image, predicting the class of the objects with the percentage accuracy score. Using OCR, computers can also recognize and convert texts in images to machine readable format like text or a document.
Object Detection and Object Recognition, on the other hand, are widely used not only in many simple applications but also complex ones like self driving cars.
This course will be a quick starter for people who want to dive into Optical Character Recognition, Image Recognition and Object Detection using Python without having to deal with all the complexities and mathematics associated with the typical Deep Learning process.
For those of you who may not be coming from a Python-based programming background, there are lectures which will help you get the basic Python programming skill to follow the course.
The code, images, and libraries used are included inside this course. You are free to use them in your projects with no questions asked.
Our Promise to You
By the end of this course, you will have learned more about OCR and object detection.
30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Computer Vision.
|Section 1 - Introduction|
|Course Introduction And Table of Contents||00:00:00|
|Introduction To OCR Concepts||00:00:00|
|Setting Up The Environment – Anaconda||00:00:00|
|Downloadable Materials – Codes And Images||00:00:00|
|Section 2 - Python Basics|
|Python Basics – Part 1 – Assignment||00:00:00|
|Python Basics – Part 2 – Flow Control||00:00:00|
|Python Basics – Part 3 – Data Structures||00:00:00|
|Python Basics – Part 4 – Functions||00:00:00|
|Section 3 - Tesseract OCR Setup|
|Tesseract OCR Setup – Part 1||00:00:00|
|Tesseract OCR Setup – Part 2||00:00:00|
|Tesseract Image OCR Implementation – Part 1||00:00:00|
|Tesseract Image OCR Implementation – Part 2||00:00:00|
|ImShow Not Responding Issue Fix||00:00:00|
|Section 4 - CNN - Convolutional Neural Networks|
|Introduction To CNN – Convolutional Neural Networks||00:00:00|
|Installing Additional Dependencies For CNN||00:00:00|
|Introduction To VGGNet Architecture||00:00:00|
|Image Recognition Using Pre-Trained VGGNet16 Model – Part 1||00:00:00|
|Image Recognition Using Pre-Trained VGGNet16 Model – Part 2||00:00:00|
|Image Recognition using Pre-Trained VGGNet19 Model||00:00:00|
|Image Recognition using Pre-Trained ResNet Model||00:00:00|
|Image Recognition using Pre-Trained Inception Model||00:00:00|
|Image Recognition using Pre-Trained Xception Model||00:00:00|
|Introduction to MobileNet-SSD Pretrained Model||00:00:00|
|Mobilenet SSD Object Detection – Part 1||00:00:00|
|Mobilenet SSD Object Detection – Part 2||00:00:00|
|Mobilenet SSD Realtime||00:00:00|
|Mobilenet SSD Video||00:00:00|
|Mask RCNN Introduction||00:00:00|
|MaskRCNN Box Implementation – Part 1||00:00:00|
|MaskRCNN Box Implementation – Part 2||00:00:00|
|MaskRCNN Mask Implementation – Part 1||00:00:00|
|MaskRCNN Mask Implementation – Part 2||00:00:00|
|MaskRCNN Realtime – Part 1||00:00:00|
|MaskRCNN Realtime – Part 2||00:00:00|
|YOLO Implementation – Part 1||00:00:00|
|YOLO Implementation – Part 2||00:00:00|
|Tiny YOLO Video||00:00:00|
|Tiny Yolo Realtime||00:00:00|