01. Document Processing Overview

Chapter 1 of 18 · 15 min

KEY INSIGHT

Document processing pipelines follow a predictable patternΓÇöconvert, clean, extract, structureΓÇöbut the choice of extraction strategy depends entirely on how the document was created. ### What This Course Covers Local AI enables processing of sensitive documents without sending data to external APIs. This course builds complete pipelines from raw PDFs through to structured data suitable for RAG systems, analytics, or database ingestion. Documents fall into two fundamental categories based on their origin. **Born-digital documents** exist as electronic filesΓÇötext PDFs exported from Word, digitally created forms, exported reports. These contain embedded text that extraction tools can read directly. **Scanned documents** exist only as imagesΓÇöphotographs of paper documents, fax transmissions, poorly scanned PDFs. These require OCR (optical character recognition) to extract any text. Understanding this distinction determines your entire processing approach. ### The Document Processing Pipeline A complete pipeline consists of five stages: 1. **Format Detection** ΓÇö Identify file type, verify it's actually a document 2. **Text Extraction** ΓÇö Pull embedded text or perform OCR 3. **Preprocessing** ΓÇö Clean artifacts, deskew, binarize images 4. **Content Analysis** ΓÇö Classify, summarize, extract entities 5. **Output Formatting** ΓÇö Structure data for downstream use Each stage has multiple implementation options with different accuracy/speed/cost tradeoffs. ### Common Failure Modes Processing pipelines fail for predictable reasons. Wrong extraction method for document type wastes time and produces garbage. Memory exhaustion occurs when processing multi-gigabyte PDFs without streaming. Encoding issues plague extracted text when source documents use unusual character sets. Garbage output from OCR typically indicates inadequate preprocessing. ### Tooling Overview This course uses these primary tools: - **PyMuPDF** ΓÇö PDF text and image extraction, metadata access - **pytesseract** ΓÇö Tesseract OCR wrapper for Python - **pdf2image + PIL** ΓÇö Image preprocessing and format conversion - **Transformers (Hugging Face)** ΓÇö Classification, summarization, NER models - **LLama.cpp / llama.cpp Python bindings** ΓÇö Local LLM inference

EXERCISE

Inspect three documents: one born-digital PDF, one scanned PDF, and one image file (JPG/PNG). For each, run the following to observe the difference:

# Check if born-digital PDF contains text
python3 -c "import fitz; doc = fitz.open('doc.pdf'); print(doc[0].get_text()[:200] if doc[0].get_text().strip() else 'NO EMBEDDED TEXT')"

# Check image properties
python3 -c "from PIL import Image; img = Image.open('doc.jpg'); print(f'Size: {img.size}, Mode: {img.mode}')"

Categorize each document as born-digital or scanned. This classification determines which extraction method you use in subsequent chapters.