{"id":6973,"date":"2026-08-10T09:39:18","date_gmt":"2026-08-10T09:39:18","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-mastering-ocr-using-deep-learning-and-opencv-python-2021-3\/"},"modified":"2026-08-10T09:39:18","modified_gmt":"2026-08-10T09:39:18","slug":"udemy-mastering-ocr-using-deep-learning-and-opencv-python-2021-3","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-mastering-ocr-using-deep-learning-and-opencv-python-2021-3\/","title":{"rendered":"Udemy \u2013 Mastering OCR using Deep Learning and OpenCV-Python 2021-3"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Mastering OCR using Deep Learning and OpenCV-Python course. In this course, we will start from the basics. First, we\u2019ll discuss optical character recognition (OCR) and why you should take the time to learn it. Then we will look at the general pipeline used in most existing OCR systems. After that, we will learn each pipeline component in detail. We\u2019ll start by learning some image preprocessing techniques commonly used in OCR systems. Then we will learn some deep learning based text recognition algorithms like EAST and CTPN. We will also implement the EAST algorithm using OpenCV-Python. In the following, we will discuss the principle of CTC, which is widely used in the development of text recognition systems. We will implement the well-known CRNN text recognition algorithm. Finally, we\u2019ll learn about the last component of the OCR pipeline, reconstruction. In this section, we will discuss the importance of reconstruction for any OCR system. We will also explore an open source OCR engine called pytesseract. Finally, we will run the full OCR pipeline to extract data from the ID document using pytesseract.<\/span><\/p>\n

What you will learn in the Mastering OCR using Deep Learning and OpenCV-Python course<\/span><\/h3>\n