Computer Vision Python OCR & Object Detection Quick Starter

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Computer Vision: Python OCR & Object Detection Quick Starter
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 44 lectures (4 hour, 28 mins) | Size: 3.08 GB

Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python
What you'll learn

Optical Character Recognition with Tesseract Library, Image Recognition using Keras, Object Recognition using MobileNet SSD, Mask R-CNN, YOLO, Tiny YOLO from static image, realtime video and pre-recorded videos using Python

Requirements

A decent configuration computer (preferably Windows) and an enthusiasm to dive into the world of OCR, Image and Object Recognition using Python

Description

Hi There!

welcome to my new course 'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series.

Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision.

Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.

Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.

This course will be a quick starter for people who wants 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 typical Deep Learning process.

Let's now see the list of interesting topics that are included in this course.

At first we will have an introductory theory session about Optical Character Recognition technology.

After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.

Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.

Then we will install the dependencies and libraries that we require to do the Optical Character Recognition. We are using Tesseract Library to do the OCR. At first we will install the Library and then its python bindings. We will also install OpenCV, which is the Open Source Computer Vision library in Python.

We also will install the Pillow library, which is the Python Image Library. Then we will have an introduction to the steps involved in the Optical Character Recognition and later will proceed with coding and implementing the OCR program. We will use few example images to do a Character Recognition testing and will verify the results.

Then we will have an introduction to Convolutional Neural Networks , which we will be using to do the Image Recognition. Here we will be classifying a full image based on the single primary object in it.

We will then proceed with installing the Keras Library which we will be using to do the Image recognition. We will be using the built in , pre-trained Models that are included in Keras. The base code in python is also provided in the Keras documentation.

At first We will be using the popular pre-trained model architecture called the VGGNet. we will have an introductory session about the architecture of VGGNet. Then we will proceed with using the pre-trained VGGNet 16 Model included in keras to do Image Recognition and classification. We will try with few sample images to check the predictions. Then will move on to a deeper VGGNet 19 Model included in keras to do Image Recognition and classification.

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