About
ArchiveLM is purpose-built for researchers, archivists, librarians, and institutions that need to unlock the value hidden in historical documents. Its multi-stage AI pipeline handles the full digitization workflow: upload scanned images (JPEG or PNG), let the AI analyze complex multi-column layouts, transcribe every article and advertisement, classify content types, and generate historically contextualized annotations. At the core is a 95%+ accuracy OCR engine that supports 5–7 column broadsheet layouts, rotated ads, tables, and faded print typical of 19th and 20th century newspapers. Article Segmentation automatically distinguishes articles, legal notices, mastheads, public announcements, and advertisements, while AI Enrichments add era-specific context to each piece. Once processed, archives become fully searchable through both keyword and semantic (vector-powered) search — finding relevant content even when exact terms don't match. The built-in RAG Librarian allows users to ask natural language questions across an entire corpus, receiving cited answers drawn directly from the digitized sources. Batch processing is streamlined via Google Drive integration, and results can be exported as Searchable PDF, ALTO/XML, JSON, or Markdown for integration with library systems, research tools, or institutional repositories. ArchiveLM is ideal for national libraries, universities, law firms, genealogists, and PhD researchers working with large primary source collections.
Key Features
- Multi-Column OCR: Reads complex 5–7 column broadsheet layouts, rotated ads, tables, and edge content with 95%+ accuracy — even on faded or degraded historical print.
- Semantic Search: Vector-powered search lets users find relevant articles by meaning rather than exact keyword matches, enabling deeper discovery across large archives.
- RAG Librarian Chat: Ask natural language questions across your entire digitized archive and receive AI-generated answers with source citations drawn from the original documents.
- AI Enrichments & Content Classification: Automatically generates historical context and era-relevant annotations for each extracted article, and classifies content as articles, ads, legal notices, mastheads, and more.
- Flexible Export & Google Drive Integration: Export results as Searchable PDF, ALTO/XML, JSON, or Markdown, and automate batch processing by connecting a shared Google Drive folder.
Use Cases
- A national library digitizing 170 years of historical newspaper archives to provide public access and enable policy research across centuries of broadsheet content.
- A PhD student uploading 10,000 pages of historical correspondence to identify social networks, influence patterns, and thematic trends using semantic search and the RAG Librarian.
- A law firm researching historical land title chains and legal genealogies by extracting and structuring century-old registry records and court case files.
- A parliamentary research team digitizing decades of legislative debates to track speaker contributions, map policy evolution, and search across legislative sessions by topic.
- A family historian tracing immigration records, birth registries, and community newspaper mentions across multiple ports and decades to reconstruct family history.
Pros
- High OCR Accuracy: Achieves 95%+ accuracy even on difficult historical layouts with multi-column formats, faded ink, and mixed content types.
- Meaning-Based Search: Semantic vector search surfaces contextually relevant results that keyword-only search would miss, making large archives far more navigable.
- Research-Ready Exports: Multiple export formats (ALTO/XML, JSON, Markdown, Searchable PDF) ensure compatibility with library systems, research pipelines, and institutional repositories.
- No-Code Batch Processing: Google Drive integration enables automated batch ingestion — archivists can drop scans in a folder and results appear in the library without manual uploads.
Cons
- Limited Free Tier: The free pilot only covers 5 pages, which is insufficient for evaluating the tool on any realistically sized archival collection.
- Narrow Domain Focus: Optimized specifically for historical documents; general-purpose document digitization or modern business document workflows are not the primary use case.
- Opaque Pricing: Paid plan details and per-page costs are not publicly listed, requiring users to visit a pricing page or contact sales for volume estimates.
Frequently Asked Questions
What file formats can I upload to ArchiveLM?
ArchiveLM accepts scanned document images in JPEG and PNG formats. You can upload files directly through the web interface or connect a Google Drive shared folder for automated batch processing.
How accurate is the OCR?
ArchiveLM achieves 95%+ OCR accuracy. Its multi-stage AI pipeline analyzes layout, transcribes content, structures it into articles, and verifies accuracy against the original scan to maintain high fidelity.
Can I search documents by meaning rather than exact keywords?
Yes. ArchiveLM includes vector-powered semantic search that finds relevant articles based on meaning, so you can discover content even when the exact words you're thinking of don't appear in the text.
What export formats does ArchiveLM support?
Results can be exported as Searchable PDF, ALTO/XML, JSON, and Markdown — covering the major formats used by library systems, digital humanities tools, and custom research pipelines.
Is there a free trial available?
Yes. ArchiveLM offers a free pilot that lets you process up to 5 pages with no credit card required, so you can evaluate the OCR quality and search features before committing to a paid plan.
