Data Extraction Basics for Docs and Images with OCR and NER_1

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:

Python: A versatile and widely used programming language for data science and machine learning

Pandas: A powerful library for data manipulation and analysis

OCR: Optical character recognition, used to convert images of text into machine-readable text

Tesseract: A popular open-source OCR engine

PyTesseract: A Python wrapper for Tesseract

OpenCV: A computer vision library

Spacy: A natural language processing (NLP) library

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:

Code walkthrough of working pipeline which performs various operations on documents such as conversion, extraction, and labeling

Line-by-line code walkthrough of various operations performed at different steps

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

Detailed explanation of steps required to train Spacy for NER

Key Topics:

Understanding Data Conversion

Conversion and Extraction from structured PDF document

Conversion of Scanned PDF document

Conversion and Extraction of data from word document

Common Format for Pipeline

Image Reading using PIL and OpenCV

Tesseract for Extraction

Tesseract Page Segmentation Mode (PSM) and OCR Engine Mode (OEM)

Extraction of Data from Image

PyTesseract Operations

Named Entity Recognition (NER)

Spacy Entity Types

IOB Format

Labelling with Spacy for NER

Training Spacy model on custom data using NER

Predicting using Trained Spacy Model

Pandas

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

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!