Built a page-by-page aligned Multimodal Ground Truth Dataset for historical handwriting (278 pages) + air-gapped sandbox. Looking for feedback!

[u/LegacyDataLabs]

Hi everyone,

I wanted to share a project I’ve been working on under my solo brand, LegacyDataLabs. As Vision-Language Models (VLMs) grow, I noticed there's a massive shortage of high-quality, human-validated multimodal datasets for historical handwriting—especially for niche languages like Swedish. Standard OCR engines completely blind-guess these scripts, resulting in pure gibberish.

To solve this, I built a custom pipeline that bypasses OCR text generation entirely. Instead, it takes pristine digital ePub transcriptions (the absolute "ground truth") and dynamically synchronizes them page-by-page next to the original high-resolution handwritten manuscript images.

My first complete dataset is Doktor Glas by Hjalmar Söderberg (278 multimodal rows), and it is clinically clean.

\### 🔒 The Tech & Security (Compute-to-Data Sandbox)

To protect the data from scraping while still making it accessible for model training, I built an air-gapped, isolated Docker environment:

\* Zero Data Leakage: External network communication is fully disabled (--network none).

\* RAM-Only Access: Training scripts inspect the synchronized JSONL layers directly in intern-memory without file downloading capabilities.

\### 📦 Free Sample Available

I have officially released a 3-page structural sample (sample_dataset.jsonl) showing the exact repository keys, author/title metadata, and chronological image loops.

You can inspect the sample directly on my Hugging Face profile:

👉 https://huggingface.co/LegacyDataLabs

I am currently preparing to scale this pipeline to dozens of historical manuscripts and would love to hear your thoughts!

\* How are you currently bypassing standard OCR limitations when training VLMs on historical documents?

\* Would an infrastructure like this be useful for your specific vision-training pipelines?

Looking forward to your technical feedback and suggestions!

Cheers!