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Data Extraction Basics for Docs and Images with OCR and NER
Rating: 3.9 out of 5(80 ratings)
409 students

Data Extraction Basics for Docs and Images with OCR and NER

Become a Data Extraction Expert with Python, Pandas, OCR, NER, and Spacy : Learn to Train and Build Real-World Solutions
Last updated 5/2025
English

What you'll 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

Course content

9 sections48 lectures2h 24m total length
  • Learning Path to become Computer Vision Expert2:37
  • Course Starter - How to approach the course6:18
  • Udemy Review1:51

Requirements

  • Basic understanding of programming
  • Familiarity with Python

Description

Master Intelligent Data Extraction with Python: A Deep Dive into OCR, NLP, and Computer Vision

Elevate your data science and machine learning skills by mastering advanced techniques for extracting valuable information from diverse document formats.

This comprehensive course is designed to equip you with the tools and knowledge to efficiently extract data from PDFs, images, and other documents. You'll delve into cutting-edge techniques in Optical Character Recognition (OCR), Natural Language Processing (NLP), and Computer Vision to automate data extraction processes and streamline your workflows.

Key Topics Covered:


  • Fundamental Image Processing Concepts:

    • Pixel-level operations

    • Image filtering and noise reduction

    • Image transformations and feature extraction

  • OCR with Tesseract:

    • Tesseract OCR engine and its configuration options

    • Image preprocessing techniques for optimal OCR performance

    • Handling complex layouts and document structures

    • Fine-tuning Tesseract for domain-specific text extraction

  • Text Extraction with PyTesseract:

    • Leveraging PyTesseract for efficient text extraction

    • Advanced PyTesseract techniques for handling challenging documents

    • Integrating PyTesseract into data pipelines

  • Natural Language Processing (NLP) with Spacy:

    • Text preprocessing and tokenization

    • Part-of-speech tagging and dependency parsing

    • Named Entity Recognition (NER) for identifying key information

    • Customizing Spacy models for specific domains

  • Building Data Extraction Pipelines:

    • Designing efficient data extraction workflows

    • Handling diverse document formats (PDF, images, Word, etc.)

    • Combining OCR, NLP, and computer vision techniques

    • Error handling and quality assurance strategies

By the end of this course, you'll be able to:

  • Extract text from complex document layouts with high accuracy

  • Build robust data extraction pipelines for various applications

  • Apply advanced NLP techniques to analyze and extract insights from text data

  • Leverage computer vision techniques to preprocess and enhance image-based documents

  • Customize and fine-tune OCR and NLP models for specific domains

Join us to unlock the power of data and gain a competitive edge in the field of data science and machine learning.

Who this course is for:

  • 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.