Text and Image Processing for Digital Archives: extracting, cleaning, analyzing, and interpreting archival collections with AI.
This module introduces a practical coding workflow for digital archive materials. Learners move from raw PDF documents and image-based pages to machine-readable text, structured classification outputs, sentiment insights, and multimodal image search.
Inspect whether archival PDFs contain a text layer, compare born-digital and scanned documents, and use OCR when text exists only as an image.
Prepare cleaned archival text for NLP and classify pages or records into meaningful categories using lightweight machine learning workflows.
Apply rule-based and pretrained language-model approaches to interpret positive, neutral, negative, or mixed sentiment in text collections.
Use CLIP-style image and text embeddings to connect visual materials with natural-language queries for retrieval and digital collection exploration.
Archive collections often mix born-digital PDFs, scanned pages, captions, images, and historical text. Choosing the right extraction method is the first step toward reliable AI analysis, because OCR noise, missing text layers, and formatting artifacts can affect every downstream result.
Complete the three guided coding tasks below. Each task includes a Colab notebook and a walkthrough video placeholder that you can replace with your final links.
Inspect digital archive PDFs, distinguish born-digital pages from scanned pages, extract text with PyMuPDF, apply OCR with pytesseract when pages are image-based, and clean the extracted text so it is ready for page-level NLP and classification workflows.
Apply sentiment analysis methods such as VADER, zero-shot classification, and pretrained transformer pipelines to interpret emotional tone in text collections.
Build an image search demo by encoding images and natural-language queries into a shared embedding space, comparing similarity scores, and returning top image matches.
🚀 After processing the archive, compare how each AI output should be interpreted!
Scenario: “Choose the right processing step.”
Learners decide whether a page needs direct text extraction, OCR, text classification, sentiment analysis, or multimodal image search.
# Example decision logic for Module 4
# Task 1: PDF extraction, OCR, and text preparation
if page_has_text_layer:
method = "extract_text_with_pymupdf"
else:
method = "run_ocr_with_tesseract"
# Task 1 continued: Text classification-ready output
classification_output = {
"page_id": "page_001",
"predicted_category": "archives",
"confidence": 0.86
}
# Task 2: Sentiment analysis
sentiment_output = {
"text": "Visitors loved the exhibit.",
"label": "positive"
}
# Task 3: Multimodal retrieval
clip_search = {
"query": "people reading in a library",
"top_result": "archive_image_03.jpg"
}