What you would learn in Optical Character Recognition (OCR) in Python course?
In the realm that is Computer Vision is the sub-area of Optical Character Recognition (OCR) that aims to transform images into text. OCR can be defined as the process of changing images with handwritten, typed, and printed texts into words that machines can comprehend. It is possible to transform photographs or scans of documents into text which can be edited using any software, like Microsoft Word. Microsoft Word. One of the most common applications is automatic form reading, where you can upload a picture from your credit or debit card or driver's license. The software will read all of your information without manually typing them. Autonomous vehicles can utilize OCR to recognize traffic signals, and parking lots will be able to guarantee access to the parking lot by scanning the license plates of vehicles!
To get you to this point, In this course, you will learn the practice of using OCR libraries to detect the text on videos and images. All the code will be executed step-by-step with this Python programming language! We will use Google Colab, which means that you won't need to think about installing the libraries on your computer since all the code will be created on Google's GPUs! Additionally, you will be taught how to create your own OCR by starting from scratch using Deep Learning and Convolutional Neural Networks! Below, you can review the major topics of the course:
Recognition of text in videos and images by using Tesseract, EasyOCR, and EAST
Look for specific words in images by using regular expressions
Techniques to improve the quality of images, for example, thresholding, color-inversion grayscale, resizing noise removal, morphological processes, and perspective transformation
EAST architecture and the EasyOCR library to provide better results in scenes that are natural
Training and OCR starting from scratch with TensorFlow and other modern Deep Learning techniques, such as Convolutional Neural Networks
Use of natural processing technologies to the text extracted from OCR (word cloud, name entity recognition)
Reading the license plate
These are only some of the major topics! At the end of the course, you'll have everything you need to build your own projects that recognize text using OCR!
Utilize Tesseract, EAST, and EasyOCR tools to recognize text in videos and images.
Learn the differences between OCR in natural and controlled settings.
Apply techniques for image pre-processing to enhance image quality, including thresholding and inversion, resizing processing morphological functions, and noise reduction
Make use of EAST architecture and the EasyOCR library for faster results in natural sceneries.
Create an OCR by starting from scratch using Deep Learning and Convolutional Neural Networks
The application of natural processing technologies to the text extracted from OCR (word cloud as well as recognized entity names)
Reading the license plate.
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