Data Extraction Basics for Docs and Images with OCR and NER

🚀 Course Title: Become a Data Extraction Expert with Python, Pandas, OCR, NER, and Spacy: Learn to Train and Build Real-World Solutions 📊📝

🔥 Course Headline: Master Smart Data Extraction from PDFs and Images with Python, Pandas, OCR, Tesseract, PyTesseract, OpenCV, Spacy, and NER

Overview: Dive into the fascinating world of data extraction and harness the power of Python, Pandas, Optical Character Recognition (OCR), Natural Language Processing (NLP), and more to intelligently pull out information from PDFs and images. This course is your gateway to mastering a suite of tools that are essential for any computer vision project.

What You'll Learn:

• Python: The programming backbone for data science and machine learning tasks.
• Pandas: A robust tool for data manipulation, cleaning, and analysis.
• OCR: Transform images of text into searchable formats with Tesseract, PyTesseract, and OpenCV.
• Spacy & NER: Leverage Spacy's powerful NLP capabilities to identify and classify entities within text.

Hands-On Learning:

• Build a common pipeline for data extraction from various document types, including PDFs and Word documents.
• Develop real-world applications with a working end product supported by prompt problem-solving assistance.
• Learn to train Spacy models tailored to your custom NER needs.

Unique Offerings:

• Step-by-step code walkthrough for a fully functional data extraction pipeline.
• Detailed guidance on training Spacy for NER, with support provided within 24 hours for any issues you encounter.

Key Topics Covered:

1. Understanding Data Conversion: The foundation of converting different document types into a standardized format.
2. Conversion and Extraction from Structured PDF Documents: Learn to handle structured data with ease.
3. Conversion of Scanned PDF Documents: Techniques to extract meaningful data even from less-structured sources.
4. Conversion and Extraction of Data from Word Documents: Master the extraction process from Microsoft Word documents.
5. Common Format for Pipeline: Ensure consistency in your data extraction pipeline.
6. Image Reading with PIL and OpenCV: Discover methods to read and process images effectively.
7. Tesseract for Extraction: Understand the nuances of Tesseract's PSM and OEM modes.
8. PyTesseract Operations: Get hands-on experience with PyTesseract to extract text from images.
9. Named Entity Recognition (NER): Identify and classify named entities in text using Spacy.
10. Spacy Entity Types: Explore the different entity types that Spacy can recognize.
11. IOB Format: Learn about the Inline-Offset-Based format for NER.
12. Labelling with Spacy for NER: Gain expertise in labeling data with Spacy for NER tasks.
13. Training Spacy Model on Custom Data Using NER: Customize your Spacy model to fit your specific needs.
14. Predicting Using Trained Spacy Model: Apply your trained models to real-world scenarios.
15. Pandas: Manipulate and analyze data efficiently with Pandas.
16. Convert Data to CSV Output: Learn to output your extracted data in a structured, usable format.

Embark on this journey to become a data extraction expert and unlock the potential of unstructured data! 🌟🔍✨