Syllabus

•Session 1 (1.5 hours): Introduction to Python: history, uses, and industry scope
•Session 2 (1.5 hours): Python installation & initial configuration
•Session 3 (1.5 hours): Getting to know the interface and basic navigation
•Session 4 (1.5 hours): Preference settings & efficient workspace
•Session 5 (1.5 hours): Main tools & menu: functions and basic usage
•Session 6 (1.5 hours): Basic practice: creating your first project
•Session 7 (1.5 hours): Import & export basic files in Python
•Session 8 (1.5 hours): Basic file storage & project management techniques
•Session 9 (1.5 hours): Simple case study: Python applications in the workplace
•Session 10 (1.5 hours): Review of basic material & comprehension quiz

Enroll in This Course

Python Course — Computer Vision – OCR Using Python

What You Will Learn

Understand Image Fundamentals and apply them to Image Processing
Learn to implement OCR – Text Detection with OpenCV and Deep Learning Models
Use Tesseract and EasyOCR to implement OCR – Text Recognition
Work with OCR – Text Labeling using Spacy and Regular Expressions
Use OpenCV and Tesseract to apply Noise Removal Techniques including Thresholding, Rescaling, Dilation, Erosion, and Deskewing
Execute Code Implementation of CTPN and EAST Models for Text Detection and Text Recognition
Build OCR Solutions for Invoice Processing, Vehicle License Plates, Business Card Recognition, and KYC Digitalization
Quick Guide on OCR Architecture, Commercial Solutions, and Industry Use Cases

Requirements

Basic Programming Skills with Python

Description

****** This course is a quick guide for those who want to become a Computer Vision – Optical Character Recognition (OCR) Specialist ******

Optical Character Recognition commonly referred to as OCR is the new keyword in the industry driving digitalization in companies. Every company wants to adopt OCR to achieve easier and faster access to their data streams in digital format. OCR implementation not only accelerates text process workflows across various industries but also helps in delivering better customer experiences. In fact, according to a recent research report, the OCR market valued at approximately 7.2 billion USD is estimated to experience significant growth in market size and is projected to reach 13.4 billion USD by 2025.

Follow this course to gain comprehensive understanding of Optical Character Recognition (OCR) for Data Extraction from Images and PDFs using Python. This course explains theory concepts followed by code demonstrations to make you an expert in OCR computer vision. It provides hands-on guidance on Text Detection with OpenCV and Deep Learning Models, Text Recognition with Tesseract and OCR along with Text Labeling through Spacy and Regular Expressions. It guides you to create technical solutions for most relevant OCR use cases in the industry.

Here are some topics we will cover:

OCR Architecture
Pixels and Image Fundamentals
Kernels and Feature Maps
Preprocessing Techniques (Binarization, Thresholding, Rescaling)

Frequently Asked Questions

Is this Python course suitable for beginners?▾

The material is arranged in stages starting from basic concepts, so participants without prior experience can still follow along with instructor guidance.

What is the learning method?▾

Live online classes via Zoom with experienced instructors, hands-on practice, and recorded sessions that can be replayed. There is also an in-house training option available for companies.

Is there a final project that can be used as a portfolio?▾

Yes, each participant completes an end-to-end mini project that can be directly used as a portfolio or thesis/dissertation material.

Do I get a certificate after completion?▾

Yes, participants who complete the program receive an official e-certificate from Cerdas Inovasi Digital.

How do I register?▾

Click the registration button on this course page or contact our admin for schedule consultation and a package that suits your needs.

Python Course/Training — Computer Vision – OCR Using Python