If you’ve ever spent a late afternoon flipping through yellowing artifact catalogs, trying to track down a specific object ID or provenance code, you’re not alone. These records—most often typewritten and filed decades ago—hold critical data for archaeologists, collections staff, and researchers. The problem? They're usually stuck on paper.

In recent years, AI transcription tools have opened up a practical alternative to manual data entry. It’s not perfect, but it’s fast, and in many cases, it’s surprisingly accurate. So if you’re sitting on shelves of site forms, catalog cards, or accession logs, here’s how to start converting them into usable spreadsheets—without burning out your intern team.

For decades, archives, museums, and archaeological organizations have relied on trained staff and volunteers to transcribe historical documents—sometimes line by line, sometimes word by word. It’s patient, meticulous work. But it’s also slow. In 2025, with pressure mounting to digitize and share collections, many teams are asking: can AI really do this better?

Step 1: Scan the Originals

Before AI can do anything, you need high-quality digital copies of your artifact catalogs. A flatbed scanner at 300dpi usually works best, but mobile scanning apps are a good fallback in the field. The key is clarity—folded corners, shadowed edges, or smudged text can trip up even the best models.

Try grouping similar forms together, labeling batches, and keeping your scans clean and cropped. Consistency goes a long way when it comes to layout detection.

Step 2: Upload to a Transcription Tool

Once you have your scans, upload them to an AI-powered transcription tool built for cultural heritage. Platforms like ArchAI recognize structured layouts typical of archaeological or museum forms—not just plain text.

You can drag and drop your files into the interface. From there, the system will analyze layout patterns and begin parsing fields automatically.

Step 3: Check the Layout Detection

The tool looks for repeated structures—like rows of entries—and tries to match fields across pages. If the formatting is consistent, this can be remarkably accurate.

Some platforms let you preview and adjust the field mapping before the full run. It’s worth spending a few minutes here to reduce errors downstream.

Step 4: Export to Spreadsheet (and Actually Use It)

Once processed, you’ll get structured output—typically as a .CSV or Excel file. Each row corresponds to one catalog entry, and columns are mapped to fields like Object ID, Provenience, Description, and more.

You can import this directly into a collections database, sort it in Excel, or prep it for GIS or research. No more copying and pasting line-by-line.

Step 5: Spot-Check and Adjust

AI tools are good—but not perfect. It’s a good idea to sample a few pages for accuracy, especially if your output is heading into a public archive or report.

Many tools will highlight uncertain fields or offer confidence scores to help focus your review. A quick QA pass ensures your data is clean, consistent, and trustworthy.

Why Not Just Use Regular OCR?

Good question. You can absolutely run typewritten documents through generic OCR tools like ABBYY, Adobe Acrobat, or even open-source options like Tesseract. And for some jobs, they’ll do okay.

But here’s the catch: those tools typically give you one long block of raw text. They don’t recognize that your catalog has rows and columns. They don’t see that one field is a description and another is an object ID.

In short, you get a rough transcript—but then you’re stuck cleaning it up manually. Copying, pasting, structuring fields… all over again.

That’s where a platform like ArchAI stands out. It doesn’t just read the text—it understands the layout. It maps data into structured spreadsheets, field by field, so your output is ready to use the moment it’s downloaded.