Digitization and Content

DIGITIZATION AND CONTENT ENRICHMENT

Turning primary source archives into structured, discoverable content, at scale

Amnet pairs purpose-built AI with human review to move books, manuscripts, newspapers, and archival papers from scanned images to publication-ready XML, accurately and on schedule.

Book a Consultation

WHO WE ARE

Two decades of digitization, run like a controlled process

Amnet delivers OCR, handwritten text recognition, XML creation, metadata enrichment, and content indexing across books, manuscripts, newspapers, periodicals, journals, and archival papers. An AI-assisted, human-verified workflow holds accuracy at scale without trading off quality.

25+

Years of digitization and content expertise

20-30

Active projects in the current pipeline

8

High-volume initiatives, each 100Ks of images

CORE SERVICES DELIVERED

Image Processing and QA

99.5 percent accuracy benchmark, verified at every batch.

Metadata Extraction and TEI XML

Structured, standards-compliant XML output.

Redaction Services

Sensitive data identified and removed to spec.

Controlled and Keyword Indexing

Taxonomy-aligned, AI-proposed, expert-reviewed.

Content Checking (UAT) and Sign-Off

Formal review gate before delivery.

AI CAPABILITY

AI-powered capability highlights

Purpose-built AI layers embedded across the digitization pipeline, paired with human review where judgment matters most.

Image Cleanup

AI-assisted dust removal, deskewing, and orientation correction ahead of conversion.

Conversion

Automated TIFF/PDF-to-JPEG conversion at custom DPI per project specification.

OCR and HTR

Machine and handwritten text recognition with bounding-box content extraction.

Content Extraction

Structured metadata capture using authority files, TOCs, and catalog data.

Article Split-Up

Automated segment- and article-level tagging within the TEI XML structure.

Controlled Indexing

AI-proposed taxonomy placement with confidence scoring, reviewed by experts.

SCOPE OF WORK

End-to-end processing of pre-digitized primary sources

Delivered as enriched, structured, quality-verified output, ready for platform ingestion.

Content Types Supported

Books and manuscripts

Periodicals: magazines, newspapers, comics, and academic journals

Archive papers (e.g., government documents)

Input Formats

Digitized images: TIFF, JPEG, or PDF

Catalogue data: Excel or XML

Project-specific “”spec“” documents

Amnet Deliverables

Quality-checked, enhanced image files

TEI XML: OCR/HTR text, metadata, article-level tagging

Redacted images, indexed content, and UAT/QA reports

METHODOLOGY

Proposed workflow and methodology

Seven controlled stages, each with a defined AI-assist point and a human quality checkpoint.

01 Input Analysis

Assess source quality; flag corrupt or incomplete files.

02 Image Conversion and QA

Convert to custom-spec JPEGs; clean up dust, skew, and orientation.

03 Metadata Extraction

Extract metadata from authority files and TOC.

04 OCR and Content Extraction

OCR/HTR extraction with content bounding boxes.

05 XML Conversion

OCR content structured into standard XML + taxonomy.

06 XML Validation and QA

Automated validation and WYSIWYG preview QA.

07 Platform Ingestion and QA

Manifest ingested; content, image, and metadata QA.

INDEXING TIERS

Content indexing tiers

Transparent depth options, indexed against your platform taxonomy, with AI carrying the load where confidence is the highest.

Controlled Subject Indexing

Option A: 200–300 terms, up to three levels.
Option B: 400–500 terms, up to five levels.

AI-ASSISTED DELIVERY

AI proposes top three taxonomy placements with a confidence score from OCR text; broader Option A leans more on AI, deeper Option B routes more toward human review.

Standard Metadata Indexing

Topic, content type, date/time period, geography, and person; person names resolved against LOC authority records.

AI-ASSISTED DELIVERY

Topic, type, date, and location are auto-extracted with high confidence; ambiguous person-name matches route to human review.

Keyword Indexing

Three to ten keywords assigned per item (issue or article) for fast, relevant discovery.

AI-ASSISTED DELIVERY

Most AI-assisted tier: an LLM ranks keyword candidates; a reviewer selects and finalizes the final list.

CASE STUDY

1 million pages, 16 weeks

A leading STEM and academic publisher: legacy scanned PDF book pages converted to client-specific XML.

The Challenge

1M legacy scanned image-PDF pages as source
Right-file assessment across a 3.5K-file repository
16-week delivery window
2.6M references to be tagged per spec
100K math equations to MathML; 100K tables to XML

The Solution

Centralized PDF-to-content extraction OCR engine
Auto-transformation of XML per specification
Auto-prediction of images and style application
Human-in-the-loop WYSIWYG review tool
Auto AI/ML reference tagging + automated Schematron validation

5→250

Titles/week scaled in six weeks

99.15%

Quality: only 17 of 2,004 titles reevaluated

2K→24K

Pages/day capacity in six weeks

CASE STUDY

A 25-year partnership, sustained

Consistent OCR-to-XML delivery for a theological publisher, sustained across two decades of volume swings.

Extraction and Conversion Workflow

OUTPUT: Color images (300 dpi PNG), Word text, plus XML/EPUB

Fast-Track Team for Ad Hoc Requests. A dedicated expert team handles 5–10 short-turnaround titles every month, keeping ad hoc priorities on schedule.

WHY AMNET

A dependable partner for long-running archive programs

AI + Human-Verified Accuracy

Quality assurance benchmarked at 99.5 percent accuracy, with structured exception reporting and rescan management.

Dedicated Project Management

A single PM as tactical lead, from kickoff to sign-off, SFTP setup, rolling delivery plans, and daily/weekly reporting.

Proven Scalability

Demonstrated ability to ramp capacity up or down, from 2,000 to 24,000 pages per day within weeks.

Long-Term Partnership

20+ year client relationships spanning STEM, academic, and theological publishing programs.