Data Extraction Basics for Docs and Images with OCR and NER
Data Extraction Basics for Docs and Images with OCR and NER, available at $44.99, has an average rating of 4.1, with 39 lectures, 1 quizzes, based on 67 reviews, and has 356 subscribers.
You will learn about Learn how to extract data from PDFs, Word docs, scanned images, and more with ease. Use Tesseract and PyTesseract to perform optical character recognition (OCR) on images with accuracy. Develop a common pipeline for data extraction from different types of input documents. Learn how to develop a robust data extraction workflow Get started on how to use Spacy efficiently for labelling Learn how to train Spacy for your own data set Use Pandas to convert extracted data to a CSV format Design a customizable technical OCR solution for data extraction This course is ideal for individuals who are Python Developers who need to extract data from various sources for their work. or Students who are interested in learning about data extraction and how it can be used to solve real-world problems or Anyone who is curious about data extraction and wants to learn more about it. It is particularly useful for Python Developers who need to extract data from various sources for their work. or Students who are interested in learning about data extraction and how it can be used to solve real-world problems or Anyone who is curious about data extraction and wants to learn more about it.
Enroll now: Data Extraction Basics for Docs and Images with OCR and NER
Summary
Title: Data Extraction Basics for Docs and Images with OCR and NER
Price: $44.99
Average Rating: 4.1
Number of Lectures: 39
Number of Quizzes: 1
Number of Published Lectures: 39
Number of Published Quizzes: 1
Number of Curriculum Items: 40
Number of Published Curriculum Objects: 40
Original Price: $89.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn how to extract data from PDFs, Word docs, scanned images, and more with ease.
- Use Tesseract and PyTesseract to perform optical character recognition (OCR) on images with accuracy.
- Develop a common pipeline for data extraction from different types of input documents.
- Learn how to develop a robust data extraction workflow
- Get started on how to use Spacy efficiently for labelling
- Learn how to train Spacy for your own data set
- Use Pandas to convert extracted data to a CSV format
- Design a customizable technical OCR solution for data extraction
Who Should Attend
- Python Developers who need to extract data from various sources for their work.
- Students who are interested in learning about data extraction and how it can be used to solve real-world problems
- Anyone who is curious about data extraction and wants to learn more about it.
Target Audiences
- Python Developers who need to extract data from various sources for their work.
- Students who are interested in learning about data extraction and how it can be used to solve real-world problems
- Anyone who is curious about data extraction and wants to learn more about it.
Master Smart Data Extraction from PDF and Images with Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy, and NER
Gain a competitive edge in the world of computer vision by learning how to extract data from PDFs and images intelligently. In this comprehensive course, you’ll learn how to use a variety of powerful tools and techniques, including:
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Python: A versatile and widely used programming language for data science and machine learning
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Pandas: A powerful library for data manipulation and analysis
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OCR: Optical character recognition, used to convert images of text into machine-readable text
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Tesseract: A popular open-source OCR engine
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PyTesseract: A Python wrapper for Tesseract
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OpenCV: A computer vision library
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Spacy: A natural language processing (NLP) library
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NER: Named entity recognition, used to identify and classify named entities in text
You’ll also learn how to build a common pipeline for data extraction from different types of input documents, including structured PDF documents, scanned PDF documents, and Word documents. By the end of the course, you’ll be able to develop robust data extraction solutions for a variety of real-world applications.
Unique Offerings:
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Code walkthrough of working pipeline which performs various operations on documents such as conversion, extraction, and labeling
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Line-by-line code walkthrough of various operations performed at different steps
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End product that you will build with us towards the end of course is in working condition and support is provided within 24 hours for any issues faced
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Detailed explanation of steps required to train Spacy for NER
Key Topics:
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Understanding Data Conversion
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Conversion and Extraction from structured PDF document
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Conversion of Scanned PDF document
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Conversion and Extraction of data from word document
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Common Format for Pipeline
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Image Reading using PIL and OpenCV
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Tesseract for Extraction
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Tesseract Page Segmentation Mode (PSM) and OCR Engine Mode (OEM)
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Extraction of Data from Image
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PyTesseract Operations
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Named Entity Recognition (NER)
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Spacy Entity Types
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IOB Format
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Labelling with Spacy for NER
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Training Spacy model on custom data using NER
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Predicting using Trained Spacy Model
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Pandas
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Convert Data to CSV Output
Course Curriculum
Chapter 1: Course Starter
Lecture 1: Learning Path to become Computer Vision Expert
Lecture 2: Course Starter – How to approach the course
Lecture 3: Udemy Review
Chapter 2: Environment Setup
Lecture 1: Objectives
Lecture 2: Tools Setup – Ubuntu
Lecture 3: Tools Setup – Windows
Lecture 4: Using Pycharm for Coding
Chapter 3: Conversion of Document to Images and Text
Lecture 1: Objectives
Lecture 2: Understanding Data Conversion
Lecture 3: Conversion and Extraction from Structured PDF document
Lecture 4: Conversion of Scanned PDF document
Lecture 5: Conversion and Extraction of data from word document
Lecture 6: Common Format for Pipeline
Lecture 7: Code Download Instructions
Chapter 4: Extraction of Data from Images using OCR
Lecture 1: Objectives
Lecture 2: Image Reading using PIL and OpenCV
Lecture 3: Tesseract for Extraction
Lecture 4: Tesseract Page Segmentation Mode (PSM) and OCR Engine Mode (OEM)
Lecture 5: PyTesseract Operations
Lecture 6: Extraction of Data From Image
Lecture 7: Code Download Instructions
Chapter 5: NLP – Training Spacy Model & Labelling Data
Lecture 1: Objectives
Lecture 2: Named Entity Recognition (NER)
Lecture 3: Introducing Spacy
Lecture 4: Spacy Entity Types
Lecture 5: IOB Format
Lecture 6: Labelling with Spacy for NER
Lecture 7: Training Spacy model on custom data using NER
Lecture 8: Predicting using Trained Spacy Model
Lecture 9: Code Download Instructions
Chapter 6: Convert Data to CSV Output using Pandas
Lecture 1: Objectives
Lecture 2: Pandas
Lecture 3: Convert Data to CSV Output
Lecture 4: Code Download Instructions
Chapter 7: Final Project
Lecture 1: Objectives
Lecture 2: Workflow Pipeline
Lecture 3: Smart Data Extractor Project
Lecture 4: Code Download Instructions
Lecture 5: More Learnings
Instructors
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Vineeta Vashistha
Technical Architect – Deep Learning
Rating Distribution
- 1 stars: 6 votes
- 2 stars: 0 votes
- 3 stars: 12 votes
- 4 stars: 7 votes
- 5 stars: 42 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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