OCR and HTR: Giving Voice to Silent Pages
Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are the engines that turn static images into searchable text.
OCR can process books, newspapers, and printed journals, turning them into fully searchable PDFs.
HTR, powered by deep learning, can read cursive and historical scripts — something that was once the domain of expert paleographers.
Digitization has been a key focus for libraries since the late 20th century, initially driven by the need to preserve rare materials and reduce physical handling. Early digitization efforts involved flatbed scanners, basic image editing, and manual transcription — processes that were slow, costly, and limited in scale. A single high-resolution scan of a rare manuscript page could take several minutes, and transcription of handwritten texts could take days for just a few pages.
The integration of Artificial Intelligence into digitization workflows marks a turning point, enabling libraries to process thousands of pages per day while enhancing quality and accessibility in ways that were previously impossible.
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. In the context of digitization in academic libraries, NLP plays a crucial role in transforming scanned documents and archival materials into searchable, analyzable, and richly described digital resources.
When libraries digitize books, manuscripts, theses, or archival records, the initial output from scanning is typically an image or PDF. While Optical Character Recognition (OCR) converts these images into machine-readable text, NLP takes the process further by:
Cleaning and normalizing text – Removing OCR errors, correcting spelling, and standardizing formatting.
Language detection – Automatically identifying the language(s) in a document.
Tokenization and lemmatization – Breaking text into meaningful units (words, sentences) and reducing them to base forms for indexing.
Named Entity Recognition (NER) – Identifying and classifying proper names (people, places, organizations, dates) for enhanced metadata.
From Dusty Manuscripts to Digital Clarity
Imagine a 200-year-old manuscript covered in faded ink and yellowed by time. Before AI, restoration required expert graphic editors spending hours on each page. Today, AI models trained on millions of document images can detect imperfections — stains, creases, uneven lighting — and correct them in seconds.
How it works behind the scenes:
The scanned image is fed into a convolutional neural network (CNN), a type of AI that processes visual data.
The model identifies areas where text is obscured or distorted.
It applies enhancement algorithms (contrast adjustment, noise removal) without altering the original content.
Automated Metadata: Turning Chaos into Order
If OCR/HTR turns a page into text, metadata turns that text into a discoverable resource. Without metadata, digital collections are like warehouses without inventory lists.
AI metadata generators use Natural Language Processing (NLP) to:
Detect people, places, dates, and events mentioned in a document.
Suggest subject categories from controlled vocabularies like the Library of Congress Subject Headings.
Create concise summaries that appear in search results.