Extracting Document Data with OCR and Custom NER in Python

Raw scanned documents and images contain valuable data, but unlocking that information requires bridging the gap between computer vision and natural language processing. This text-based course guides you through the process of building an intelligent document parsing pipeline. You will learn how to clean document images, extract raw text, and train a custom Named Entity Recognition (NER) model to automatically identify and structure crucial data points. What you'll learn: - Understand the foundational concepts of computer vision, optical character recognition (OCR), and natural language processing. - Clean and preprocess document images using OpenCV to optimize them for text extraction. - Extract text from images using Pytesseract and format it for downstream processing. - Label text data manually using the BIO (Inside-Outside-Beginning) tagging schema for custom entity extraction. - Train a custom Named Entity Recognition (NER) model using modern SpaCy configuration pipelines. - Structure extracted text into clean, validated data formats using modern Python validation techniques. We begin with the core definitions and setup of your Python environment. Next, you will progress through image preprocessing, OCR text extraction, manual text labeling, and training your custom NLP model, concluding with structuring your extracted data. This course is designed for beginner Python developers, data enthusiasts, and aspiring machine learning engineers, requiring only basic Python knowledge to start. Start reading today to turn unstructured document images into clean, actionable data.