This course is a quick starter for anyone who wants to explore optical character recognition (OCR), image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process.Starting with an introduction to the OCR technology, you'll get your system ready for Python coding by installing Anaconda packages and the necessary libraries and dependencies.
As you advance, you'll work with convolutional neural networks (CNNs), the Keras library, and pre-trained models such as VGGNet 16 and VGGNet 19, to perform image recognition with the help of sample images. The course then focuses on object recognition and shows you how to use MobileNet-SSD and Mask R-CNN pre-trained models to detect and label objects in a real-time live video from the computer's webcam as well as in a saved video. Toward the end, you'll learn how the YOLO model and the lite version, Tiny YOLO, fasten the process of detecting an object from a single image.
By the end of the course, you'll have developed a solid understanding of OCR and the methods involved and gain the confidence to perform optical character recognition using Python with ease.
All resources and code files for this course are placed here: https://github.com/PacktPublishing/Computer-Vision-Python-OCR-Object-De…
Install Anaconda packages, dependencies, and libraries such as Tesseract, OpenCV, pillow
Get to grips with optical character recognition in Python using the tesseract library
Perform image recognition using VGGNet 16, VGGNet 19, ResNet, Inception, and Xception pre-trained models in the Keras library
Explore object recognition using MobileNet SSD, Mask R-CNN, YOLO
Achieve a perfect blend of speed and accuracy in object detection and recognition
Learn about optical character recognition with tesseract library and image recognition using Keras